In [2]:
# Python ≥3.5 is required
import sys
assert sys.version_info >= (3, 5)

# Scikit-Learn ≥0.20 is required
import sklearn
assert sklearn.__version__ >= "0.20"

# TensorFlow ≥2.0 is required
import tensorflow as tf
from tensorflow import keras
assert tf.__version__ >= "2.0"

# Common imports
import numpy as np
import os

# To plot pretty figures
%matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot as plt
mpl.rc('axes', labelsize=14)
mpl.rc('xtick', labelsize=12)
mpl.rc('ytick', labelsize=12)

import pandas as pd

Load data

In [3]:
def readtimes(file):
    times = []; values = []
    with open(file) as f:
        for line in f:
            if 'groups' in line:
                groups = line.replace(',',' ').split()[5:]
            if 'TIME' in line:
                newtime = True
                break
                
        while newtime:
            newtime = False
            times.append(float(line.replace(',',' ').split()[2]))

            values_per_time = []
            for line in f:
                if 'GROUP' in line:
                    continue
                if 'TIME' in line:
                    newtime = True
                    break
                values_per_time.append([float(x) for x in line.strip().split(',') if x != ''])  
            values.append(values_per_time)
        return times, groups, values
In [4]:
times, groups, values = readtimes('group-output-time_mod.csv')
In [5]:
times = np.array(times)
values = np.array(values)

print('# of times: ', len(times))
print('# of groups: ', len(groups))
print('values.shape: ', values.shape)
# of times:  3888
# of groups:  8
values.shape:  (3888, 80, 10)
In [6]:
fig, ax = plt.subplots(2,4, figsize=[20,10])
for i, group in enumerate(groups):
    im = ax.flatten()[i].imshow(values[3090,i*10:(i+1)*10,:])
    fig.colorbar(im, ax=ax.flatten()[i])
    ax.flatten()[i].set_title(group)
In [7]:
fig, ax = plt.subplots(2,4, figsize=[20,10])
for i, group in enumerate(groups):
    ax.flatten()[i].plot(times, values[:,i*10,4])
    ax.flatten()[i].set_title(group)

Prepare the data

In [8]:
nl = int(values.shape[-1])
nc = int(values.shape[-2]/len(groups))
print('Grid: ', nl, 'x',nc)
Grid:  10 x 10
In [9]:
X_train_2D = values
In [10]:
X_train_2D.shape
Out[10]:
(3888, 80, 10)
In [11]:
X_train_3D = X_train_2D.reshape(len(times),len(groups),nl,nc)
In [12]:
X_train_3D.shape 
Out[12]:
(3888, 8, 10, 10)
In [13]:
X_train_1D = X_train_2D.reshape(len(times),len(groups)*nl*nc)
In [14]:
X_train_1D.shape
Out[14]:
(3888, 800)
In [15]:
from sklearn.preprocessing import StandardScaler

stdscaler = StandardScaler()

X_train_1D_norm = stdscaler.fit_transform(X_train_1D)
X_train_2D_norm = X_train_1D_norm.reshape(len(times),len(groups)*nl, nc)
X_train_3D_norm = X_train_1D_norm.reshape(len(times),len(groups),nl,nc)
In [16]:
def calculateerror(X_train_3D, X_train_3D_recovered, groups, print_step = 0):
    abs_error = abs(X_train_3D - X_train_3D_recovered)
    perc_error = abs_error*100/abs(X_train_3D)
    
    print('max_abs_error: ',np.max(abs(X_train_3D - X_train_3D_recovered)) )
    print('mean_abs_error: ',np.mean(abs(X_train_3D - X_train_3D_recovered)) )
    
    if print_step:
        for time in range(0,X_train_3D.shape[0],print_step):
            print('\ntime: ',time)
            for i, group in enumerate(groups):
                print('Group '+group+': max_abs_error = ',
                      round(np.max(abs_error[time,i,:,:]) ,4),
                      ' %_mae = ',
                      round( np.max(perc_error[time,i,::][np.isfinite(perc_error[time,i,::])]) ,4),
                      '%')

Dimensionality reduction - PCA

In [17]:
from sklearn.decomposition import PCA

pca = PCA(X_train_1D.shape[1])
X_train_pca = pca.fit_transform(X_train_1D)
In [18]:
X_recovered = pca.inverse_transform(X_train_pca)
np.allclose(X_recovered, X_train_1D)
Out[18]:
True
In [19]:
#print(pca.singular_values_**2/(X_train_1D.shape[0]-1))
#print()
#print(pca.explained_variance_)
#print(pca.explained_variance_ratio_)
In [20]:
p = 0.999
cumsum_eig = np.cumsum(pca.explained_variance_ratio_)
d = np.argmax(cumsum_eig >= p) + 1
d
Out[20]:
6
In [21]:
plt.figure(figsize=(6,4))
plt.plot(cumsum_eig, linewidth=3)
plt.xlabel("Dimensions")
plt.ylabel("Explained Variance")
plt.ylim([cumsum_eig[0],1.1])
plt.plot([d, d], [0, p], "k:")
plt.plot([0, d], [p, p], "k:")
plt.plot(d, p, "ko")
plt.annotate("Elbow", xy=(d, p), xytext=(d, cumsum_eig[0]+0.05),
             arrowprops=dict(arrowstyle="->"), fontsize=16)
plt.grid(True)
plt.show()
In [22]:
p = 0.999
cumsum_sv = np.cumsum(pca.singular_values_/sum(pca.singular_values_))
d = np.argmax(cumsum_sv >= p) + 1
d
Out[22]:
15
In [23]:
plt.figure(figsize=(6,4))
plt.plot(cumsum_sv, linewidth=3)
plt.xlabel("Dimensions")
plt.ylabel("Cumsum Singular values")
plt.ylim([cumsum_sv[0],1.1])
plt.plot([d, d], [0, p], "k:")
plt.plot([0, d], [p, p], "k:")
plt.plot(d, p, "ko")
plt.annotate("Elbow", xy=(d, p), xytext=(d, cumsum_sv[0]+0.05),
             arrowprops=dict(arrowstyle="->"), fontsize=16)
plt.grid(True)
plt.savefig('pca_normCumSum_singularValues.png')
plt.show()

Build the model - PCA

In [24]:
pca_compress = PCA(n_components=15)
X_train_pca = pca_compress.fit_transform(X_train_1D)
X_recovered = pca_compress.inverse_transform(X_train_pca)
np.allclose(X_recovered, X_train_1D)
Out[24]:
False
In [25]:
fig, ax = plt.subplots(1,1, figsize=[20,10])
ax.plot(times, X_train_pca);
ax.grid()
ax.legend(range(15))
Out[25]:
<matplotlib.legend.Legend at 0x7faff5a0d710>
In [26]:
import joblib
joblib.dump(pca_compress, "pca_compress_15.pkl") 
np.savetxt('X_train_1D.csv', X_train_1D, delimiter=',') 
np.savetxt('X_train_pca.csv', X_train_pca, delimiter=',') 
np.savetxt('times.csv', times, delimiter=',') 
with open('groups.txt','w') as f:
    f.writelines([g + '\n' for g in groups])

#...
# pca_compress = joblib.load("pca_compress_15.pkl") 
# X_train_compressed = np.loadtxt('X_train_pca.csv', delimiter=',') 

# X_train_1D = np.loadtxt('X_train_1D.csv', delimiter=',') 
# times  = np.loadtxt('times.csv', delimiter=',') 
# with open('groups.txt') as f:
#     groups = [g.strip() for g in f.readlines()]

# # PCA recovered
# X_recovered = pca_compress.inverse_transform(X_train_compressed)
In [27]:
calculateerror(X_train_1D.reshape(len(times),len(groups),nl,nc), 
               X_recovered.reshape(len(times),len(groups),nl,nc), 
               groups,
               print_step=0)
max_abs_error:  0.9455268950468039
mean_abs_error:  0.003775904514174
/home/viluiz/anaconda3/envs/py3ml/lib/python3.7/site-packages/ipykernel_launcher.py:3: RuntimeWarning: divide by zero encountered in true_divide
  This is separate from the ipykernel package so we can avoid doing imports until
/home/viluiz/anaconda3/envs/py3ml/lib/python3.7/site-packages/ipykernel_launcher.py:3: RuntimeWarning: invalid value encountered in true_divide
  This is separate from the ipykernel package so we can avoid doing imports until
In [28]:
fig, ax = plt.subplots(2,4, figsize=[20,10])
for i, group in enumerate(groups):
    im = ax.flatten()[i].imshow(X_train_1D.reshape(len(times),len(groups),nl,nc)[100,i,:,:])
    fig.colorbar(im, ax=ax.flatten()[i])
    ax.flatten()[i].set_title(group)
In [29]:
fig, ax = plt.subplots(2,4, figsize=[20,10])
for i, group in enumerate(groups):
    im = ax.flatten()[i].imshow(X_recovered.reshape(len(times),len(groups),nl,nc)[100,i,:,:])
    fig.colorbar(im, ax=ax.flatten()[i])
    ax.flatten()[i].set_title(group)
In [30]:
fig, ax = plt.subplots(2,4, figsize=[20,10])
for i, group in enumerate(groups):
    ax.flatten()[i].plot(times, X_train_1D[:,i*nl*nc+4])
    ax.flatten()[i].set_title(group)
In [31]:
fig, ax = plt.subplots(2,4, figsize=[20,10])
for i, group in enumerate(groups):
    ax.flatten()[i].plot(times, X_recovered[:,i*nl*nc+4])
    ax.flatten()[i].set_title(group)
In [32]:
fig, ax = plt.subplots(2,4, figsize=[20,10])
for i, group in enumerate(groups):
    ax.flatten()[i].plot(times, X_train_1D[:,i*nl*nc+4])
    ax.flatten()[i].plot(times, X_recovered[:,i*nl*nc+4],'--')
    ax.flatten()[i].set_title(group)
plt.savefig('pca_compression.png')
pca_compress = PCA(n_components=15) X_train_pca = pca_compress.fit_transform(X_train_1D_norm) X_recovered_norm = pca_compress.inverse_transform(X_train_pca) X_recovered_norm = stdscaler.inverse_transform(X_recovered_norm) np.allclose(X_recovered_norm, X_train_1D)calculateerror(X_train_1D.reshape(len(times),len(groups),nl,nc), X_recovered_norm.reshape(len(times),len(groups),nl,nc), groups, print_step=0)

Dimensionality reduction - DEEP AUTOENCODER

Linear autoencoder

In [30]:
np.random.seed(42)
tf.random.set_seed(42)

# Need to have validation loss
early_stopping = keras.callbacks.EarlyStopping(monitor='val_loss',
                                               min_delta=0.0,
                                               patience=100,
                                               verbose=2,
                                               restore_best_weights=True)

encoder = keras.models.Sequential([keras.layers.Dense(15, input_shape=[800])])
decoder = keras.models.Sequential([keras.layers.Dense(800, input_shape=[15])])
autoencoder = keras.models.Sequential([encoder, decoder])

autoencoder.compile(loss="mse", 
                    optimizer=keras.optimizers.Nadam(lr=0.0007, beta_1=0.9, beta_2=0.999)
                    )
autoencoder.summary()
Model: "sequential_2"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
sequential (Sequential)      (None, 15)                12015     
_________________________________________________________________
sequential_1 (Sequential)    (None, 800)               12800     
=================================================================
Total params: 24,815
Trainable params: 24,815
Non-trainable params: 0
_________________________________________________________________
In [31]:
history = autoencoder.fit(X_train_1D_norm, 
                          X_train_1D_norm, 
                          epochs=1000,
                          validation_data=(X_train_1D_norm, X_train_1D_norm),
                          callbacks=[early_stopping])
Train on 3888 samples, validate on 3888 samples
Epoch 1/1000
3888/3888 [==============================] - 1s 284us/sample - loss: 0.0789 - val_loss: 0.0311
Epoch 2/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 0.0196 - val_loss: 0.0143
Epoch 3/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 0.0112 - val_loss: 0.0080
Epoch 4/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 0.0058 - val_loss: 0.0043
Epoch 5/1000
3888/3888 [==============================] - 0s 108us/sample - loss: 0.0034 - val_loss: 0.0028
Epoch 6/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 0.0021 - val_loss: 0.0019
Epoch 7/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 0.0019 - val_loss: 0.0018
Epoch 8/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 0.0018 - val_loss: 0.0017
Epoch 9/1000
3888/3888 [==============================] - 0s 106us/sample - loss: 0.0017 - val_loss: 0.0017
Epoch 10/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 0.0016 - val_loss: 0.0017
Epoch 11/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 0.0015 - val_loss: 0.0014
Epoch 12/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 0.0013 - val_loss: 0.0013
Epoch 13/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 0.0012 - val_loss: 0.0010
Epoch 14/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 9.3700e-04 - val_loss: 8.1995e-04
Epoch 15/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 7.5246e-04 - val_loss: 6.6059e-04
Epoch 16/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 7.5002e-04 - val_loss: 5.4606e-04
Epoch 17/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 5.1014e-04 - val_loss: 4.6631e-04
Epoch 18/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 4.3811e-04 - val_loss: 4.0917e-04
Epoch 19/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 4.6926e-04 - val_loss: 3.6424e-04
Epoch 20/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 3.3174e-04 - val_loss: 2.9551e-04
Epoch 21/1000
3888/3888 [==============================] - 0s 106us/sample - loss: 2.5156e-04 - val_loss: 2.0485e-04
Epoch 22/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 1.7204e-04 - val_loss: 1.3412e-04
Epoch 23/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 1.8479e-04 - val_loss: 8.2656e-05
Epoch 24/1000
3888/3888 [==============================] - 0s 108us/sample - loss: 6.9879e-05 - val_loss: 6.2360e-05
Epoch 25/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 1.1980e-04 - val_loss: 4.5659e-05
Epoch 26/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 4.8073e-05 - val_loss: 4.2629e-05
Epoch 27/1000
3888/3888 [==============================] - 0s 106us/sample - loss: 3.8982e-05 - val_loss: 3.8475e-05
Epoch 28/1000
3888/3888 [==============================] - 0s 108us/sample - loss: 8.9628e-05 - val_loss: 3.7922e-05
Epoch 29/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 3.6395e-05 - val_loss: 3.6244e-05
Epoch 30/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 6.0429e-05 - val_loss: 4.7494e-04
Epoch 31/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 4.7577e-05 - val_loss: 3.4907e-05
Epoch 32/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 4.4244e-05 - val_loss: 4.7000e-05
Epoch 33/1000
3888/3888 [==============================] - 0s 106us/sample - loss: 1.4359e-04 - val_loss: 3.5843e-05
Epoch 34/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 3.5460e-05 - val_loss: 3.6388e-05
Epoch 35/1000
3888/3888 [==============================] - 0s 106us/sample - loss: 3.6164e-05 - val_loss: 3.6501e-05
Epoch 36/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 8.8671e-05 - val_loss: 3.5552e-05
Epoch 37/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 3.5901e-05 - val_loss: 3.6773e-05
Epoch 38/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 8.2072e-05 - val_loss: 3.5609e-05
Epoch 39/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 3.5580e-05 - val_loss: 3.4665e-05
Epoch 40/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 4.5434e-05 - val_loss: 5.5702e-05
Epoch 41/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 3.9772e-05 - val_loss: 3.5167e-05
Epoch 42/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 6.9462e-05 - val_loss: 0.0012
Epoch 43/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 9.3966e-05 - val_loss: 3.4895e-05
Epoch 44/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 3.6265e-05 - val_loss: 5.9290e-05
Epoch 45/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 3.7843e-05 - val_loss: 3.6437e-05
Epoch 46/1000
3888/3888 [==============================] - 0s 106us/sample - loss: 6.4081e-05 - val_loss: 3.5740e-05
Epoch 47/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 9.0530e-05 - val_loss: 3.4496e-05
Epoch 48/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 3.6090e-05 - val_loss: 3.7044e-05
Epoch 49/1000
3888/3888 [==============================] - 0s 108us/sample - loss: 8.3593e-05 - val_loss: 3.5292e-05
Epoch 50/1000
3888/3888 [==============================] - 0s 106us/sample - loss: 3.5681e-05 - val_loss: 3.4150e-05
Epoch 51/1000
3888/3888 [==============================] - 0s 106us/sample - loss: 3.7302e-05 - val_loss: 3.5704e-05
Epoch 52/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 6.3372e-05 - val_loss: 3.6931e-05
Epoch 53/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 3.7722e-05 - val_loss: 3.5659e-05
Epoch 54/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 6.5542e-05 - val_loss: 4.1661e-05
Epoch 55/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 6.5800e-05 - val_loss: 3.4699e-05
Epoch 56/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 3.5152e-05 - val_loss: 3.6531e-05
Epoch 57/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 4.1610e-05 - val_loss: 3.7624e-05
Epoch 58/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 5.3220e-05 - val_loss: 3.2487e-04
Epoch 59/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 4.4849e-05 - val_loss: 4.0178e-05
Epoch 60/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 6.2060e-05 - val_loss: 3.6755e-05
Epoch 61/1000
3888/3888 [==============================] - 0s 106us/sample - loss: 3.8183e-05 - val_loss: 3.6130e-05
Epoch 62/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 1.3652e-04 - val_loss: 3.8014e-05
Epoch 63/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 3.5360e-05 - val_loss: 3.4471e-05
Epoch 64/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 3.5025e-05 - val_loss: 3.5150e-05
Epoch 65/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 4.2616e-05 - val_loss: 3.6003e-05
Epoch 66/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 3.9803e-05 - val_loss: 3.8005e-05
Epoch 67/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 4.8341e-05 - val_loss: 1.2148e-04
Epoch 68/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 3.5740e-05 - val_loss: 3.7297e-05
Epoch 69/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 4.8722e-05 - val_loss: 3.5301e-05
Epoch 70/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 4.4143e-05 - val_loss: 3.4342e-05
Epoch 71/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 3.9729e-05 - val_loss: 3.5244e-05
Epoch 72/1000
3888/3888 [==============================] - 0s 108us/sample - loss: 5.9585e-05 - val_loss: 3.4516e-05
Epoch 73/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 5.4770e-05 - val_loss: 3.6336e-05
Epoch 74/1000
3888/3888 [==============================] - 0s 108us/sample - loss: 3.5945e-05 - val_loss: 3.8451e-05
Epoch 75/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 4.4603e-05 - val_loss: 3.4487e-05
Epoch 76/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 3.6313e-05 - val_loss: 3.5674e-05
Epoch 77/1000
3888/3888 [==============================] - 0s 106us/sample - loss: 6.9294e-05 - val_loss: 3.3895e-05
Epoch 78/1000
3888/3888 [==============================] - 0s 106us/sample - loss: 3.7692e-05 - val_loss: 3.4662e-05
Epoch 79/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 4.0735e-05 - val_loss: 0.0014
Epoch 80/1000
3888/3888 [==============================] - 0s 108us/sample - loss: 7.9858e-05 - val_loss: 3.3427e-05
Epoch 81/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 3.6114e-05 - val_loss: 3.5275e-05
Epoch 82/1000
3888/3888 [==============================] - 0s 108us/sample - loss: 4.0342e-05 - val_loss: 3.3311e-05
Epoch 83/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 4.1208e-05 - val_loss: 3.2646e-05
Epoch 84/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 3.5828e-05 - val_loss: 3.3693e-05
Epoch 85/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 4.6290e-05 - val_loss: 4.0048e-05
Epoch 86/1000
3888/3888 [==============================] - 0s 108us/sample - loss: 4.6160e-05 - val_loss: 3.4210e-05
Epoch 87/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 4.0259e-05 - val_loss: 3.4632e-05
Epoch 88/1000
3888/3888 [==============================] - 0s 106us/sample - loss: 6.9006e-05 - val_loss: 2.9006e-04
Epoch 89/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 3.8540e-05 - val_loss: 3.3131e-05
Epoch 90/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 3.3114e-05 - val_loss: 3.3281e-05
Epoch 91/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 5.1074e-05 - val_loss: 3.1536e-05
Epoch 92/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 3.1837e-05 - val_loss: 3.2542e-05
Epoch 93/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 3.2386e-05 - val_loss: 3.2199e-05
Epoch 94/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 3.4039e-05 - val_loss: 1.3513e-04
Epoch 95/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 4.5875e-05 - val_loss: 3.1827e-05
Epoch 96/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 4.0488e-05 - val_loss: 3.3459e-05
Epoch 97/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 3.1311e-05 - val_loss: 3.2085e-05
Epoch 98/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 5.9925e-05 - val_loss: 3.0731e-05
Epoch 99/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 3.3661e-05 - val_loss: 3.4685e-05
Epoch 100/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 3.5204e-05 - val_loss: 2.9887e-05
Epoch 101/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 2.9129e-05 - val_loss: 2.9274e-05
Epoch 102/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 4.2479e-05 - val_loss: 2.7587e-05
Epoch 103/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 2.7714e-05 - val_loss: 3.0007e-05
Epoch 104/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 3.2525e-05 - val_loss: 4.2280e-05
Epoch 105/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 3.6839e-05 - val_loss: 2.7956e-05
Epoch 106/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 4.0425e-05 - val_loss: 2.5926e-05
Epoch 107/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 2.7498e-05 - val_loss: 2.5288e-05
Epoch 108/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 2.8758e-05 - val_loss: 2.3848e-05
Epoch 109/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 3.4998e-05 - val_loss: 2.3406e-05
Epoch 110/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 5.1984e-05 - val_loss: 2.6967e-05
Epoch 111/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 2.2264e-05 - val_loss: 2.2215e-05
Epoch 112/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 2.1111e-05 - val_loss: 2.2008e-05
Epoch 113/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 2.3385e-05 - val_loss: 6.6078e-05
Epoch 114/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 2.5740e-05 - val_loss: 1.9789e-05
Epoch 115/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 2.4469e-05 - val_loss: 2.0019e-05
Epoch 116/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 3.4200e-05 - val_loss: 4.7974e-05
Epoch 117/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 1.7359e-05 - val_loss: 1.5383e-05
Epoch 118/1000
3888/3888 [==============================] - 0s 106us/sample - loss: 2.0317e-05 - val_loss: 1.5642e-05
Epoch 119/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 1.8341e-05 - val_loss: 2.2351e-05
Epoch 120/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 2.5985e-05 - val_loss: 1.3608e-05
Epoch 121/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 1.4301e-05 - val_loss: 8.1672e-05
Epoch 122/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 3.8747e-05 - val_loss: 1.2251e-05
Epoch 123/1000
3888/3888 [==============================] - 0s 106us/sample - loss: 1.1870e-05 - val_loss: 1.1693e-05
Epoch 124/1000
3888/3888 [==============================] - 0s 106us/sample - loss: 2.0123e-05 - val_loss: 1.0866e-05
Epoch 125/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 1.1942e-05 - val_loss: 1.0216e-05
Epoch 126/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 1.3834e-05 - val_loss: 1.0210e-05
Epoch 127/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 3.4737e-05 - val_loss: 5.6461e-04
Epoch 128/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 2.8131e-05 - val_loss: 8.9750e-06
Epoch 129/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 1.2382e-05 - val_loss: 8.3610e-06
Epoch 130/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 8.4563e-06 - val_loss: 9.2463e-06
Epoch 131/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 9.7800e-06 - val_loss: 8.2702e-06
Epoch 132/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 1.2191e-05 - val_loss: 8.2624e-06
Epoch 133/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 1.6390e-05 - val_loss: 9.5165e-06
Epoch 134/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 8.8670e-06 - val_loss: 7.6018e-06
Epoch 135/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 8.6599e-06 - val_loss: 8.1054e-06
Epoch 136/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 2.1363e-05 - val_loss: 7.6530e-06
Epoch 137/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 1.0099e-05 - val_loss: 9.3766e-06
Epoch 138/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 1.5444e-05 - val_loss: 9.9354e-06
Epoch 139/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 1.3068e-05 - val_loss: 9.1268e-06
Epoch 140/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 2.7341e-05 - val_loss: 1.3050e-05
Epoch 141/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 6.7956e-06 - val_loss: 8.0892e-06
Epoch 142/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 7.4466e-06 - val_loss: 5.9785e-06
Epoch 143/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 7.8923e-06 - val_loss: 5.5913e-06
Epoch 144/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 7.6612e-06 - val_loss: 6.7927e-06
Epoch 145/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 3.8833e-05 - val_loss: 5.5189e-06
Epoch 146/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 5.7010e-06 - val_loss: 5.5431e-06
Epoch 147/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 7.0593e-06 - val_loss: 5.7793e-06
Epoch 148/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 4.6730e-05 - val_loss: 1.1371e-05
Epoch 149/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 5.7481e-06 - val_loss: 5.1823e-06
Epoch 150/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 6.0099e-06 - val_loss: 5.3031e-06
Epoch 151/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 5.3487e-06 - val_loss: 6.5288e-06
Epoch 152/1000
3888/3888 [==============================] - 0s 106us/sample - loss: 5.3411e-06 - val_loss: 5.5283e-06
Epoch 153/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 6.1459e-06 - val_loss: 5.5785e-06
Epoch 154/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 7.2542e-06 - val_loss: 6.0966e-06
Epoch 155/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 3.8356e-05 - val_loss: 5.7171e-06
Epoch 156/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 5.2852e-06 - val_loss: 5.3407e-06
Epoch 157/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 6.0022e-06 - val_loss: 5.3271e-06
Epoch 158/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 7.3480e-06 - val_loss: 5.0394e-06
Epoch 159/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 5.4610e-06 - val_loss: 1.5985e-05
Epoch 160/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 1.1606e-05 - val_loss: 2.1315e-05
Epoch 161/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 1.5169e-05 - val_loss: 5.4833e-06
Epoch 162/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 6.7518e-06 - val_loss: 6.9106e-06
Epoch 163/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 2.0344e-05 - val_loss: 5.3913e-06
Epoch 164/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 9.8774e-06 - val_loss: 5.2832e-06
Epoch 165/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 6.1030e-06 - val_loss: 1.4954e-05
Epoch 166/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 4.1978e-05 - val_loss: 5.0282e-06
Epoch 167/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 5.0524e-06 - val_loss: 5.8656e-06
Epoch 168/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 5.7221e-06 - val_loss: 5.0438e-06
Epoch 169/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 5.1784e-06 - val_loss: 5.9204e-06
Epoch 170/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 5.5255e-06 - val_loss: 5.8209e-06
Epoch 171/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 1.2727e-05 - val_loss: 5.1070e-06
Epoch 172/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 7.5459e-06 - val_loss: 7.6614e-06
Epoch 173/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 1.1730e-05 - val_loss: 5.6874e-06
Epoch 174/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 7.8309e-06 - val_loss: 4.1466e-05
Epoch 175/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 2.9474e-05 - val_loss: 1.0766e-05
Epoch 176/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 5.3732e-06 - val_loss: 7.3699e-06
Epoch 177/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 7.0468e-06 - val_loss: 5.3402e-06
Epoch 178/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 5.7789e-06 - val_loss: 5.8221e-06
Epoch 179/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 1.6730e-05 - val_loss: 5.5838e-06
Epoch 180/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 1.1135e-05 - val_loss: 4.9319e-06
Epoch 181/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 5.8148e-06 - val_loss: 5.0332e-06
Epoch 182/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 1.6090e-05 - val_loss: 4.9500e-06
Epoch 183/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 5.4679e-06 - val_loss: 5.1305e-06
Epoch 184/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 9.0764e-06 - val_loss: 5.1413e-06
Epoch 185/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 7.8881e-06 - val_loss: 5.0183e-05
Epoch 186/1000
3888/3888 [==============================] - ETA: 0s - loss: 1.9354e-0 - 0s 104us/sample - loss: 1.8154e-05 - val_loss: 5.8104e-06
Epoch 187/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 5.5169e-06 - val_loss: 5.2057e-06
Epoch 188/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 1.9678e-05 - val_loss: 5.0411e-06
Epoch 189/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 8.0556e-06 - val_loss: 5.4868e-06
Epoch 190/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 6.4279e-06 - val_loss: 1.1003e-05
Epoch 191/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 1.6429e-05 - val_loss: 4.8661e-06
Epoch 192/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 5.3609e-06 - val_loss: 5.0455e-06
Epoch 193/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 1.9611e-05 - val_loss: 4.9101e-06
Epoch 194/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 5.1925e-06 - val_loss: 5.2322e-06
Epoch 195/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 5.1665e-06 - val_loss: 5.0881e-06
Epoch 196/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 2.0807e-05 - val_loss: 5.3182e-06
Epoch 197/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 6.0223e-06 - val_loss: 5.2297e-06
Epoch 198/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 9.3938e-06 - val_loss: 3.5664e-05
Epoch 199/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 1.2875e-05 - val_loss: 5.3033e-06
Epoch 200/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 5.9190e-06 - val_loss: 4.9229e-06
Epoch 201/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 9.9934e-06 - val_loss: 5.6191e-05
Epoch 202/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 9.2285e-06 - val_loss: 5.3683e-06
Epoch 203/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 1.2548e-05 - val_loss: 5.0914e-06
Epoch 204/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 1.1665e-05 - val_loss: 5.6549e-06
Epoch 205/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 6.1949e-06 - val_loss: 5.7994e-06
Epoch 206/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 6.7325e-06 - val_loss: 7.5197e-06
Epoch 207/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 1.5780e-05 - val_loss: 8.6178e-06
Epoch 208/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 1.1125e-05 - val_loss: 5.7816e-06
Epoch 209/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 6.8802e-06 - val_loss: 5.1415e-06
Epoch 210/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 8.7255e-06 - val_loss: 5.9121e-06
Epoch 211/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 1.1670e-05 - val_loss: 2.0571e-05
Epoch 212/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 2.4861e-05 - val_loss: 5.3848e-06
Epoch 213/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 5.3916e-06 - val_loss: 4.8968e-06
Epoch 214/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 6.6951e-06 - val_loss: 5.0218e-06
Epoch 215/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 5.8693e-06 - val_loss: 1.4233e-05
Epoch 216/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 1.5011e-05 - val_loss: 5.5303e-06
Epoch 217/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 5.1716e-06 - val_loss: 5.3302e-06
Epoch 218/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 8.8016e-06 - val_loss: 5.1528e-06
Epoch 219/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 4.4235e-05 - val_loss: 2.0132e-05
Epoch 220/1000
3888/3888 [==============================] - 0s 98us/sample - loss: 5.3403e-06 - val_loss: 4.9542e-06
Epoch 221/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 5.0615e-06 - val_loss: 4.9248e-06
Epoch 222/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 8.0626e-06 - val_loss: 5.2801e-06
Epoch 223/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 8.1077e-06 - val_loss: 5.0242e-06
Epoch 224/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 5.1627e-06 - val_loss: 5.1841e-06
Epoch 225/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 8.0086e-06 - val_loss: 5.1013e-06
Epoch 226/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 1.0874e-05 - val_loss: 4.9499e-06
Epoch 227/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 5.4019e-06 - val_loss: 4.9033e-06
Epoch 228/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 2.5019e-05 - val_loss: 1.0005e-05
Epoch 229/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 5.2218e-06 - val_loss: 5.2911e-06
Epoch 230/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 6.7086e-06 - val_loss: 5.7231e-06
Epoch 231/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 1.0339e-05 - val_loss: 5.2158e-06
Epoch 232/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 7.3617e-06 - val_loss: 5.4608e-06
Epoch 233/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 2.6889e-05 - val_loss: 6.4740e-06
Epoch 234/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 5.0588e-06 - val_loss: 4.9012e-06
Epoch 235/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 5.0122e-06 - val_loss: 5.3816e-06
Epoch 236/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 1.1021e-05 - val_loss: 2.9005e-05
Epoch 237/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 6.0423e-06 - val_loss: 4.9029e-06
Epoch 238/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 1.2460e-05 - val_loss: 3.0004e-05
Epoch 239/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 5.7703e-06 - val_loss: 1.3937e-05
Epoch 240/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 1.4046e-05 - val_loss: 4.8118e-06
Epoch 241/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 1.4862e-05 - val_loss: 6.2507e-06
Epoch 242/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 5.2830e-06 - val_loss: 3.9124e-05
Epoch 243/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 6.0836e-06 - val_loss: 5.5407e-06
Epoch 244/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 5.4845e-06 - val_loss: 5.0267e-06
Epoch 245/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 3.9226e-05 - val_loss: 4.9706e-06
Epoch 246/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 6.0994e-06 - val_loss: 5.0257e-06
Epoch 247/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 4.8879e-06 - val_loss: 5.0373e-06
Epoch 248/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 1.0028e-05 - val_loss: 5.0836e-06
Epoch 249/1000
3888/3888 [==============================] - 0s 98us/sample - loss: 5.0379e-06 - val_loss: 8.0278e-06
Epoch 250/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 9.4061e-06 - val_loss: 5.1660e-06
Epoch 251/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 1.7664e-05 - val_loss: 4.8208e-06
Epoch 252/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 4.8100e-06 - val_loss: 5.7327e-06
Epoch 253/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 5.1340e-06 - val_loss: 4.9193e-06
Epoch 254/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 5.1229e-06 - val_loss: 1.8027e-05
Epoch 255/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 1.1558e-05 - val_loss: 3.0126e-05
Epoch 256/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 9.3252e-06 - val_loss: 5.0730e-05
Epoch 257/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 9.1598e-06 - val_loss: 5.0862e-06
Epoch 258/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 2.2504e-05 - val_loss: 4.7575e-06
Epoch 259/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 4.8357e-06 - val_loss: 5.1366e-06
Epoch 260/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 6.3199e-06 - val_loss: 4.7551e-06
Epoch 261/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 5.1272e-06 - val_loss: 5.1383e-06
Epoch 262/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 9.4567e-06 - val_loss: 5.1478e-06
Epoch 263/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 1.2456e-05 - val_loss: 4.8305e-06
Epoch 264/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 1.0631e-05 - val_loss: 5.5981e-06
Epoch 265/1000
3888/3888 [==============================] - 0s 108us/sample - loss: 7.5412e-06 - val_loss: 4.8673e-06
Epoch 266/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 8.2073e-06 - val_loss: 4.9963e-06
Epoch 267/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 1.3912e-05 - val_loss: 3.4565e-05
Epoch 268/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 1.0892e-05 - val_loss: 5.1535e-06
Epoch 269/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 4.9878e-06 - val_loss: 1.4471e-05
Epoch 270/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 9.5390e-06 - val_loss: 6.0100e-06
Epoch 271/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 3.7552e-05 - val_loss: 4.8317e-06
Epoch 272/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 4.7545e-06 - val_loss: 4.7075e-06
Epoch 273/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 4.8024e-06 - val_loss: 5.9206e-06
Epoch 274/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 5.8709e-06 - val_loss: 5.8493e-06
Epoch 275/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 5.6395e-06 - val_loss: 5.4998e-06
Epoch 276/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 6.7663e-06 - val_loss: 1.1723e-05
Epoch 277/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 9.0486e-06 - val_loss: 5.1678e-06
Epoch 278/1000
3888/3888 [==============================] - 0s 106us/sample - loss: 1.4844e-05 - val_loss: 4.8489e-06
Epoch 279/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 4.7815e-06 - val_loss: 4.5568e-06
Epoch 280/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 7.0476e-06 - val_loss: 5.4193e-06
Epoch 281/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 6.2321e-06 - val_loss: 6.5845e-06
Epoch 282/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 2.1679e-05 - val_loss: 4.5588e-06
Epoch 283/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 4.8484e-06 - val_loss: 1.1857e-05
Epoch 284/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 1.1235e-05 - val_loss: 5.2455e-06
Epoch 285/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 5.0642e-06 - val_loss: 5.0168e-06
Epoch 286/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 1.0744e-05 - val_loss: 5.8064e-06
Epoch 287/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 5.0585e-06 - val_loss: 4.4615e-06
Epoch 288/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 2.3119e-05 - val_loss: 5.0394e-06
Epoch 289/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 5.7098e-06 - val_loss: 4.8525e-06
Epoch 290/1000
3888/3888 [==============================] - 0s 106us/sample - loss: 5.1392e-06 - val_loss: 4.9212e-06
Epoch 291/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 5.4483e-06 - val_loss: 1.6513e-05
Epoch 292/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 1.1831e-05 - val_loss: 7.4398e-06
Epoch 293/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 7.0615e-06 - val_loss: 9.5160e-05
Epoch 294/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 8.3151e-06 - val_loss: 5.4201e-06
Epoch 295/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 3.2590e-05 - val_loss: 5.0781e-06
Epoch 296/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 5.2337e-06 - val_loss: 4.8531e-06
Epoch 297/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 6.3205e-06 - val_loss: 5.0338e-06
Epoch 298/1000
3888/3888 [==============================] - 0s 108us/sample - loss: 5.5627e-06 - val_loss: 5.1430e-06
Epoch 299/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 8.3596e-06 - val_loss: 4.8956e-06
Epoch 300/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 4.7784e-06 - val_loss: 4.6550e-06
Epoch 301/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 7.9813e-06 - val_loss: 4.9171e-06
Epoch 302/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 9.5712e-06 - val_loss: 5.7721e-06
Epoch 303/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 6.4205e-06 - val_loss: 3.0211e-05
Epoch 304/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 3.2759e-05 - val_loss: 4.7967e-06
Epoch 305/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 4.5602e-06 - val_loss: 4.3862e-06
Epoch 306/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 4.7119e-06 - val_loss: 4.8089e-06
Epoch 307/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 4.6186e-06 - val_loss: 4.4769e-06
Epoch 308/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 6.6707e-06 - val_loss: 4.9086e-06
Epoch 309/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 1.0940e-05 - val_loss: 3.2448e-05
Epoch 310/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 5.4742e-06 - val_loss: 6.6267e-06
Epoch 311/1000
3888/3888 [==============================] - 0s 98us/sample - loss: 1.4628e-05 - val_loss: 1.3005e-05
Epoch 312/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 4.8071e-06 - val_loss: 4.4598e-06
Epoch 313/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 1.4748e-05 - val_loss: 4.6374e-06
Epoch 314/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 4.5457e-06 - val_loss: 4.5341e-06
Epoch 315/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 1.9733e-05 - val_loss: 3.6640e-05
Epoch 316/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 5.5945e-06 - val_loss: 4.4687e-06
Epoch 317/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 4.6621e-06 - val_loss: 5.8700e-06
Epoch 318/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 7.8755e-06 - val_loss: 4.7917e-06
Epoch 319/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 5.1828e-06 - val_loss: 1.0281e-05
Epoch 320/1000
3888/3888 [==============================] - 0s 108us/sample - loss: 7.8109e-06 - val_loss: 4.4651e-06
Epoch 321/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 7.9188e-06 - val_loss: 3.5262e-05
Epoch 322/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 7.7983e-06 - val_loss: 4.9800e-06
Epoch 323/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 1.5288e-05 - val_loss: 5.2221e-06
Epoch 324/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 5.2345e-06 - val_loss: 4.5551e-06
Epoch 325/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 7.6294e-06 - val_loss: 4.8709e-06
Epoch 326/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 8.1027e-06 - val_loss: 4.9070e-06
Epoch 327/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 1.1108e-05 - val_loss: 4.6897e-06
Epoch 328/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 6.7311e-06 - val_loss: 6.5796e-06
Epoch 329/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 1.5100e-05 - val_loss: 6.6755e-06
Epoch 330/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 4.6961e-06 - val_loss: 4.6745e-06
Epoch 331/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 4.7323e-06 - val_loss: 5.5427e-06
Epoch 332/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 1.4180e-05 - val_loss: 8.6167e-06
Epoch 333/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 5.3702e-06 - val_loss: 3.9747e-06
Epoch 334/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 1.2711e-05 - val_loss: 4.1174e-06
Epoch 335/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 4.5462e-06 - val_loss: 7.7838e-06
Epoch 336/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 7.7333e-06 - val_loss: 4.3035e-06
Epoch 337/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 1.3112e-05 - val_loss: 4.4719e-06
Epoch 338/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 1.3166e-05 - val_loss: 5.1118e-06
Epoch 339/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 4.4038e-06 - val_loss: 4.7201e-06
Epoch 340/1000
3888/3888 [==============================] - 0s 98us/sample - loss: 4.3270e-06 - val_loss: 4.3371e-06
Epoch 341/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 2.3634e-05 - val_loss: 4.0785e-06
Epoch 342/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 4.4121e-06 - val_loss: 1.1417e-05
Epoch 343/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 4.4710e-06 - val_loss: 4.0805e-06
Epoch 344/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 9.5649e-06 - val_loss: 4.1110e-06
Epoch 345/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 4.2755e-06 - val_loss: 4.2250e-06
Epoch 346/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 4.4370e-06 - val_loss: 4.2863e-06
Epoch 347/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 1.1886e-05 - val_loss: 5.9942e-06
Epoch 348/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 7.2320e-06 - val_loss: 4.3304e-06
Epoch 349/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 5.8923e-06 - val_loss: 4.5553e-06
Epoch 350/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 1.8129e-05 - val_loss: 4.2498e-06
Epoch 351/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 4.9521e-06 - val_loss: 4.3294e-06
Epoch 352/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 7.0268e-06 - val_loss: 8.3489e-06
Epoch 353/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 6.0720e-06 - val_loss: 4.4433e-06
Epoch 354/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 6.9607e-06 - val_loss: 3.9563e-06
Epoch 355/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 6.6156e-06 - val_loss: 4.5936e-06
Epoch 356/1000
3888/3888 [==============================] - 0s 106us/sample - loss: 2.3178e-05 - val_loss: 4.3993e-06
Epoch 357/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 4.3873e-06 - val_loss: 3.8517e-06
Epoch 358/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 5.5082e-06 - val_loss: 4.6624e-06
Epoch 359/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 7.4649e-06 - val_loss: 3.9509e-06
Epoch 360/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 5.7694e-06 - val_loss: 6.2845e-06
Epoch 361/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 5.2815e-06 - val_loss: 6.3419e-05
Epoch 362/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 6.9849e-06 - val_loss: 4.6487e-06
Epoch 363/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 1.0594e-05 - val_loss: 3.7533e-06
Epoch 364/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 1.4823e-05 - val_loss: 3.7482e-06
Epoch 365/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 3.9819e-06 - val_loss: 4.0929e-06
Epoch 366/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 1.6992e-05 - val_loss: 3.7075e-06
Epoch 367/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 5.7237e-06 - val_loss: 3.9358e-06
Epoch 368/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 4.8160e-06 - val_loss: 4.5256e-06
Epoch 369/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 4.1589e-06 - val_loss: 3.6337e-06
Epoch 370/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 1.3154e-05 - val_loss: 4.2920e-06
Epoch 371/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 6.4231e-06 - val_loss: 3.8406e-06
Epoch 372/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 5.9863e-06 - val_loss: 4.4476e-06
Epoch 373/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 9.6583e-06 - val_loss: 3.6589e-06
Epoch 374/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 4.0727e-06 - val_loss: 4.7525e-06
Epoch 375/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 1.1493e-05 - val_loss: 3.7454e-06
Epoch 376/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 3.8636e-06 - val_loss: 3.5814e-06
Epoch 377/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 5.3165e-06 - val_loss: 5.8521e-06
Epoch 378/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 1.3439e-05 - val_loss: 6.5195e-06
Epoch 379/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 4.8515e-06 - val_loss: 5.9324e-06
Epoch 380/1000
3888/3888 [==============================] - 0s 98us/sample - loss: 5.3268e-06 - val_loss: 1.0163e-05
Epoch 381/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 1.0390e-05 - val_loss: 1.2135e-05
Epoch 382/1000
3888/3888 [==============================] - 0s 94us/sample - loss: 6.1042e-06 - val_loss: 3.5871e-06
Epoch 383/1000
3888/3888 [==============================] - 0s 97us/sample - loss: 2.9887e-05 - val_loss: 3.6318e-06
Epoch 384/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 3.4001e-06 - val_loss: 3.6702e-06
Epoch 385/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 3.4465e-06 - val_loss: 3.7085e-06
Epoch 386/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 4.1381e-06 - val_loss: 3.6344e-06
Epoch 387/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 3.8798e-06 - val_loss: 8.8591e-06
Epoch 388/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 8.1566e-06 - val_loss: 3.4785e-06
Epoch 389/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 1.0242e-05 - val_loss: 3.8277e-06
Epoch 390/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 3.4108e-06 - val_loss: 3.4363e-06
Epoch 391/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 5.6431e-06 - val_loss: 3.5601e-06
Epoch 392/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 5.2422e-06 - val_loss: 3.6854e-06
Epoch 393/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 1.3519e-05 - val_loss: 3.3585e-06
Epoch 394/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 3.8850e-06 - val_loss: 3.5937e-06
Epoch 395/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 6.6792e-06 - val_loss: 3.5633e-06
Epoch 396/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 1.7257e-05 - val_loss: 1.2816e-05
Epoch 397/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 4.1464e-06 - val_loss: 4.6950e-06
Epoch 398/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 1.3323e-05 - val_loss: 5.1388e-06
Epoch 399/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 3.4786e-06 - val_loss: 3.6109e-06
Epoch 400/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 3.4506e-06 - val_loss: 3.2491e-06
Epoch 401/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 3.9185e-06 - val_loss: 3.3070e-06
Epoch 402/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 8.1559e-06 - val_loss: 3.3327e-06
Epoch 403/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 1.8180e-05 - val_loss: 5.2763e-06
Epoch 404/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 3.7062e-06 - val_loss: 6.2080e-06
Epoch 405/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 5.6076e-06 - val_loss: 3.2970e-06
Epoch 406/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 4.0792e-06 - val_loss: 3.0986e-06
Epoch 407/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 5.5252e-06 - val_loss: 9.7515e-06
Epoch 408/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 1.8623e-05 - val_loss: 3.0030e-06
Epoch 409/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 3.1283e-06 - val_loss: 3.3845e-06
Epoch 410/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 3.5109e-06 - val_loss: 4.4202e-06
Epoch 411/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 4.1167e-06 - val_loss: 6.5078e-06
Epoch 412/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 4.9641e-06 - val_loss: 3.2515e-06
Epoch 413/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 1.7106e-05 - val_loss: 3.1336e-06
Epoch 414/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 3.4925e-06 - val_loss: 1.4723e-05
Epoch 415/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 5.0289e-06 - val_loss: 5.8728e-06
Epoch 416/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 4.1501e-06 - val_loss: 3.8416e-06
Epoch 417/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 7.5721e-06 - val_loss: 3.2010e-06
Epoch 418/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 7.2196e-06 - val_loss: 4.1598e-06
Epoch 419/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 2.0746e-05 - val_loss: 3.4600e-06
Epoch 420/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 3.2259e-06 - val_loss: 2.9926e-06
Epoch 421/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 3.4002e-06 - val_loss: 3.2789e-06
Epoch 422/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 4.1587e-06 - val_loss: 3.0755e-06
Epoch 423/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 8.1442e-06 - val_loss: 3.6701e-06
Epoch 424/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 1.0343e-05 - val_loss: 3.1752e-06
Epoch 425/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 3.1246e-06 - val_loss: 5.6706e-06
Epoch 426/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 4.8480e-06 - val_loss: 3.0396e-06
Epoch 427/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 3.6480e-06 - val_loss: 5.6841e-06
Epoch 428/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 1.0143e-05 - val_loss: 4.2139e-06
Epoch 429/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 7.6166e-06 - val_loss: 5.0772e-06
Epoch 430/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 1.0705e-05 - val_loss: 3.1488e-06
Epoch 431/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 3.8952e-06 - val_loss: 3.7206e-06
Epoch 432/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 1.0225e-05 - val_loss: 3.1733e-06
Epoch 433/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 3.1882e-06 - val_loss: 2.8599e-06
Epoch 434/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 3.6224e-06 - val_loss: 3.8469e-06
Epoch 435/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 9.3014e-06 - val_loss: 4.2564e-05
Epoch 436/1000
3888/3888 [==============================] - 0s 98us/sample - loss: 7.3309e-06 - val_loss: 3.3455e-06
Epoch 437/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 4.2192e-06 - val_loss: 3.4716e-06
Epoch 438/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 1.2653e-05 - val_loss: 2.5595e-05
Epoch 439/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 3.7822e-06 - val_loss: 3.9752e-06
Epoch 440/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 6.7849e-06 - val_loss: 3.1251e-06
Epoch 441/1000
3888/3888 [==============================] - 0s 106us/sample - loss: 3.5016e-06 - val_loss: 5.4101e-06
Epoch 442/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 1.3127e-05 - val_loss: 3.2529e-06
Epoch 443/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 8.1172e-06 - val_loss: 4.0893e-06
Epoch 444/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 1.4186e-05 - val_loss: 2.9175e-06
Epoch 445/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 4.8729e-06 - val_loss: 1.9069e-05
Epoch 446/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 3.4402e-06 - val_loss: 3.1424e-06
Epoch 447/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 6.4015e-06 - val_loss: 2.7400e-06
Epoch 448/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 1.3899e-05 - val_loss: 4.7651e-06
Epoch 449/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 3.0546e-06 - val_loss: 2.9096e-06
Epoch 450/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 3.4416e-06 - val_loss: 9.3531e-06
Epoch 451/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 5.0130e-06 - val_loss: 2.8515e-06
Epoch 452/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 5.1875e-06 - val_loss: 3.8936e-06
Epoch 453/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 9.3976e-06 - val_loss: 2.7165e-06
Epoch 454/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 3.6241e-06 - val_loss: 3.0325e-05
Epoch 455/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 8.4714e-06 - val_loss: 3.1062e-06
Epoch 456/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 7.8800e-06 - val_loss: 7.4775e-06
Epoch 457/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 1.1552e-05 - val_loss: 3.0370e-06
Epoch 458/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 3.7559e-06 - val_loss: 2.9865e-06
Epoch 459/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 9.4194e-06 - val_loss: 3.4532e-06
Epoch 460/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 2.8423e-06 - val_loss: 2.8869e-06
Epoch 461/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 3.5850e-06 - val_loss: 2.9619e-06
Epoch 462/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 8.0454e-06 - val_loss: 2.6364e-06
Epoch 463/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 5.5436e-06 - val_loss: 8.0159e-06
Epoch 464/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 1.7730e-05 - val_loss: 5.0846e-06
Epoch 465/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 3.6755e-06 - val_loss: 2.8201e-06
Epoch 466/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 5.6932e-06 - val_loss: 3.1177e-06
Epoch 467/1000
3888/3888 [==============================] - 0s 98us/sample - loss: 7.1501e-06 - val_loss: 3.7080e-06
Epoch 468/1000
3888/3888 [==============================] - 0s 98us/sample - loss: 3.0300e-06 - val_loss: 2.5705e-06
Epoch 469/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 3.0099e-06 - val_loss: 3.0578e-06
Epoch 470/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 4.9435e-06 - val_loss: 3.6098e-05
Epoch 471/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 1.2938e-05 - val_loss: 2.6384e-06
Epoch 472/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 4.4135e-06 - val_loss: 2.6095e-06
Epoch 473/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 3.1528e-06 - val_loss: 3.1163e-06
Epoch 474/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 1.5405e-05 - val_loss: 2.7423e-06
Epoch 475/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 2.7039e-06 - val_loss: 2.5798e-06
Epoch 476/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 3.6428e-06 - val_loss: 2.8517e-06
Epoch 477/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 9.6302e-06 - val_loss: 4.5265e-06
Epoch 478/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 3.3244e-06 - val_loss: 3.0128e-06
Epoch 479/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 6.3652e-06 - val_loss: 2.8536e-06
Epoch 480/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 5.8021e-06 - val_loss: 2.9453e-06
Epoch 481/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 9.1304e-06 - val_loss: 1.0012e-05
Epoch 482/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 3.2842e-06 - val_loss: 3.4515e-06
Epoch 483/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 5.1865e-06 - val_loss: 7.6181e-06
Epoch 484/1000
3888/3888 [==============================] - 0s 106us/sample - loss: 4.8024e-06 - val_loss: 2.6804e-06
Epoch 485/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 1.5376e-05 - val_loss: 2.5777e-06
Epoch 486/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 2.8003e-06 - val_loss: 2.8430e-06
Epoch 487/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 9.6118e-06 - val_loss: 3.4135e-06
Epoch 488/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 2.9655e-06 - val_loss: 2.6527e-06
Epoch 489/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 4.9998e-06 - val_loss: 2.8318e-06
Epoch 490/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 5.0941e-06 - val_loss: 5.0448e-06
Epoch 491/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 2.1511e-05 - val_loss: 3.2270e-06
Epoch 492/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 2.8192e-06 - val_loss: 2.7097e-06
Epoch 493/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 2.6374e-06 - val_loss: 2.4748e-06
Epoch 494/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 3.1791e-06 - val_loss: 2.8073e-06
Epoch 495/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 7.0822e-06 - val_loss: 2.7407e-06
Epoch 496/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 3.5561e-06 - val_loss: 2.6496e-06
Epoch 497/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 1.1056e-05 - val_loss: 7.5975e-06
Epoch 498/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 3.6853e-06 - val_loss: 2.8118e-06
Epoch 499/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 1.5395e-05 - val_loss: 2.7285e-06
Epoch 500/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 2.6706e-06 - val_loss: 2.4853e-06
Epoch 501/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 5.2579e-06 - val_loss: 2.4449e-06
Epoch 502/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 4.1775e-06 - val_loss: 2.7373e-06
Epoch 503/1000
3888/3888 [==============================] - 0s 98us/sample - loss: 3.2772e-06 - val_loss: 2.8525e-06
Epoch 504/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 6.4795e-06 - val_loss: 1.2114e-05
Epoch 505/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 4.1784e-06 - val_loss: 2.4157e-06
Epoch 506/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 6.4070e-06 - val_loss: 2.8884e-06
Epoch 507/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 8.4590e-06 - val_loss: 2.4394e-06
Epoch 508/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 3.9955e-06 - val_loss: 2.4539e-06
Epoch 509/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 1.6140e-05 - val_loss: 1.7622e-04
Epoch 510/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 7.1826e-06 - val_loss: 2.6850e-06
Epoch 511/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 2.6511e-06 - val_loss: 2.6438e-06
Epoch 512/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 5.3145e-06 - val_loss: 2.6619e-06
Epoch 513/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 2.6595e-06 - val_loss: 3.0094e-06
Epoch 514/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 1.7332e-05 - val_loss: 2.8006e-06
Epoch 515/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 2.5385e-06 - val_loss: 3.1653e-06
Epoch 516/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 2.7602e-06 - val_loss: 2.7518e-06
Epoch 517/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 3.7818e-06 - val_loss: 3.2569e-06
Epoch 518/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 6.3988e-06 - val_loss: 2.5112e-06
Epoch 519/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 3.0732e-06 - val_loss: 2.6304e-06
Epoch 520/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 9.0399e-06 - val_loss: 2.6416e-06
Epoch 521/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 4.1050e-06 - val_loss: 2.8422e-06
Epoch 522/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 3.0254e-05 - val_loss: 2.4614e-06
Epoch 523/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 2.3949e-06 - val_loss: 2.3782e-06
Epoch 524/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 2.4348e-06 - val_loss: 2.4890e-06
Epoch 525/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 3.0332e-06 - val_loss: 3.2098e-05
Epoch 526/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 6.8127e-06 - val_loss: 2.6270e-06
Epoch 527/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 2.5216e-06 - val_loss: 2.6958e-06
Epoch 528/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 5.8618e-06 - val_loss: 2.9628e-06
Epoch 529/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 2.6415e-06 - val_loss: 3.1317e-06
Epoch 530/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 5.0715e-06 - val_loss: 1.6132e-05
Epoch 531/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 8.1260e-06 - val_loss: 1.5898e-04
Epoch 532/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 1.0176e-05 - val_loss: 2.2813e-06
Epoch 533/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 2.4526e-06 - val_loss: 2.6152e-06
Epoch 534/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 4.4169e-06 - val_loss: 3.2822e-06
Epoch 535/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 5.2861e-06 - val_loss: 2.4041e-06
Epoch 536/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 3.9049e-06 - val_loss: 3.2604e-06
Epoch 537/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 9.4201e-06 - val_loss: 2.7821e-06
Epoch 538/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 3.5130e-06 - val_loss: 7.6638e-06
Epoch 539/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 5.9377e-06 - val_loss: 2.9142e-06
Epoch 540/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 3.9291e-06 - val_loss: 2.6368e-06
Epoch 541/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 5.7780e-06 - val_loss: 1.5474e-04
Epoch 542/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 1.4491e-05 - val_loss: 2.4348e-06
Epoch 543/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 2.5306e-06 - val_loss: 2.6341e-06
Epoch 544/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 7.2642e-06 - val_loss: 2.5398e-06
Epoch 545/1000
3888/3888 [==============================] - 0s 98us/sample - loss: 2.4919e-06 - val_loss: 3.2287e-06
Epoch 546/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 3.2073e-06 - val_loss: 1.1846e-05
Epoch 547/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 1.0164e-05 - val_loss: 2.4015e-06
Epoch 548/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 3.8959e-06 - val_loss: 3.4314e-06
Epoch 549/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 5.8666e-06 - val_loss: 2.7536e-06
Epoch 550/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 9.3627e-06 - val_loss: 2.3535e-06
Epoch 551/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 2.3647e-06 - val_loss: 2.2738e-06
Epoch 552/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 4.0272e-06 - val_loss: 2.3279e-06
Epoch 553/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 1.0694e-05 - val_loss: 2.8632e-06
Epoch 554/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 2.4053e-06 - val_loss: 7.4176e-06
Epoch 555/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 8.9225e-06 - val_loss: 2.2161e-06
Epoch 556/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 2.2146e-06 - val_loss: 2.3061e-06
Epoch 557/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 2.8858e-06 - val_loss: 3.3058e-06
Epoch 558/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 6.7328e-06 - val_loss: 3.8837e-06
Epoch 559/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 6.4267e-06 - val_loss: 2.0716e-06
Epoch 560/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 3.5461e-06 - val_loss: 9.9572e-06
Epoch 561/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 8.2315e-06 - val_loss: 2.1343e-06
Epoch 562/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 7.6250e-06 - val_loss: 2.1587e-06
Epoch 563/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 3.8253e-06 - val_loss: 2.1548e-06
Epoch 564/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 4.0960e-06 - val_loss: 2.7204e-06
Epoch 565/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 3.4280e-06 - val_loss: 1.0212e-05
Epoch 566/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 1.1501e-05 - val_loss: 2.2023e-06
Epoch 567/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 2.9056e-06 - val_loss: 2.1986e-06
Epoch 568/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 8.9194e-06 - val_loss: 2.1831e-06
Epoch 569/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 4.3222e-06 - val_loss: 2.0476e-06
Epoch 570/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 3.3505e-06 - val_loss: 2.1675e-06
Epoch 571/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 3.4766e-06 - val_loss: 2.4266e-06
Epoch 572/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 1.0426e-05 - val_loss: 2.5058e-06
Epoch 573/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 5.7065e-06 - val_loss: 6.6395e-06
Epoch 574/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 3.1668e-06 - val_loss: 3.0820e-06
Epoch 575/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 2.9130e-06 - val_loss: 2.0596e-06
Epoch 576/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 5.2683e-06 - val_loss: 6.1442e-06
Epoch 577/1000
3888/3888 [==============================] - 0s 98us/sample - loss: 5.5059e-06 - val_loss: 2.1132e-06
Epoch 578/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 4.9460e-06 - val_loss: 1.9898e-06
Epoch 579/1000
3888/3888 [==============================] - 0s 98us/sample - loss: 8.2428e-06 - val_loss: 2.1241e-06
Epoch 580/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 2.7587e-06 - val_loss: 2.2469e-06
Epoch 581/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 9.8901e-06 - val_loss: 2.0694e-06
Epoch 582/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 4.3967e-06 - val_loss: 1.9195e-06
Epoch 583/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 3.1953e-06 - val_loss: 2.1298e-06
Epoch 584/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 4.9616e-06 - val_loss: 2.4264e-06
Epoch 585/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 3.3160e-06 - val_loss: 2.9170e-06
Epoch 586/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 5.6424e-06 - val_loss: 2.1540e-06
Epoch 587/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 6.2708e-06 - val_loss: 1.9728e-04
Epoch 588/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 7.6577e-06 - val_loss: 1.9258e-06
Epoch 589/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 3.8769e-06 - val_loss: 2.5844e-06
Epoch 590/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 6.1559e-06 - val_loss: 2.2710e-06
Epoch 591/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 2.2033e-06 - val_loss: 2.4007e-06
Epoch 592/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 5.2184e-06 - val_loss: 2.5670e-05
Epoch 593/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 5.4938e-06 - val_loss: 2.3584e-06
Epoch 594/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 6.8161e-06 - val_loss: 3.0545e-06
Epoch 595/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 6.5915e-06 - val_loss: 1.2822e-05
Epoch 596/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 3.2210e-06 - val_loss: 3.1632e-06
Epoch 597/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 4.9176e-06 - val_loss: 2.1339e-06
Epoch 598/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 5.6785e-06 - val_loss: 3.1807e-06
Epoch 599/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 2.3373e-06 - val_loss: 1.4535e-05
Epoch 600/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 6.7131e-06 - val_loss: 1.7612e-06
Epoch 601/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 2.6269e-06 - val_loss: 3.0432e-06
Epoch 602/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 1.0552e-05 - val_loss: 1.8769e-06
Epoch 603/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 4.5103e-06 - val_loss: 2.0281e-06
Epoch 604/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 1.0693e-05 - val_loss: 2.0279e-06
Epoch 605/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 2.0089e-06 - val_loss: 1.9233e-06
Epoch 606/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 3.7832e-06 - val_loss: 1.7517e-06
Epoch 607/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 1.9150e-06 - val_loss: 3.2185e-06
Epoch 608/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 8.2165e-06 - val_loss: 2.7827e-06
Epoch 609/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 3.1336e-06 - val_loss: 1.6807e-06
Epoch 610/1000
3888/3888 [==============================] - 0s 106us/sample - loss: 3.0444e-06 - val_loss: 1.9298e-06
Epoch 611/1000
3888/3888 [==============================] - 0s 106us/sample - loss: 4.6912e-06 - val_loss: 2.3497e-06
Epoch 612/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 4.3254e-06 - val_loss: 4.1531e-06
Epoch 613/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 1.3826e-05 - val_loss: 1.5368e-04
Epoch 614/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 3.7142e-06 - val_loss: 1.8502e-06
Epoch 615/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 2.5239e-06 - val_loss: 2.0249e-06
Epoch 616/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 2.0531e-06 - val_loss: 2.0639e-06
Epoch 617/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 3.9105e-06 - val_loss: 2.9217e-06
Epoch 618/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 8.1239e-06 - val_loss: 1.8108e-06
Epoch 619/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 3.4310e-06 - val_loss: 1.9587e-06
Epoch 620/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 3.7611e-06 - val_loss: 1.5661e-06
Epoch 621/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 2.1052e-06 - val_loss: 3.2501e-06
Epoch 622/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 7.3067e-06 - val_loss: 2.9066e-05
Epoch 623/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 5.4670e-06 - val_loss: 1.6921e-06
Epoch 624/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 4.3248e-06 - val_loss: 1.6399e-06
Epoch 625/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 4.2929e-06 - val_loss: 1.5643e-06
Epoch 626/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 1.0256e-05 - val_loss: 1.6999e-06
Epoch 627/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 1.9151e-06 - val_loss: 1.6844e-06
Epoch 628/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 4.6571e-06 - val_loss: 1.7718e-06
Epoch 629/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 3.6319e-06 - val_loss: 2.9774e-06
Epoch 630/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 3.1562e-06 - val_loss: 3.1268e-06
Epoch 631/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 5.0154e-06 - val_loss: 1.6568e-06
Epoch 632/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 9.9970e-06 - val_loss: 3.3753e-06
Epoch 633/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 2.0242e-06 - val_loss: 1.4710e-06
Epoch 634/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 1.9737e-06 - val_loss: 5.0970e-06
Epoch 635/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 6.7710e-06 - val_loss: 1.9928e-06
Epoch 636/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 2.9441e-06 - val_loss: 1.5312e-06
Epoch 637/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 2.8332e-06 - val_loss: 1.5876e-06
Epoch 638/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 2.0395e-05 - val_loss: 1.4889e-06
Epoch 639/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 1.8108e-06 - val_loss: 9.1968e-06
Epoch 640/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 2.2025e-06 - val_loss: 2.6203e-06
Epoch 641/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 2.5283e-06 - val_loss: 1.5452e-06
Epoch 642/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 1.6146e-06 - val_loss: 3.4278e-06
Epoch 643/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 3.7904e-06 - val_loss: 1.4155e-06
Epoch 644/1000
3888/3888 [==============================] - 0s 98us/sample - loss: 1.1134e-05 - val_loss: 1.5511e-06
Epoch 645/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 1.8921e-06 - val_loss: 1.5820e-06
Epoch 646/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 2.7583e-06 - val_loss: 1.7527e-06
Epoch 647/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 2.2230e-05 - val_loss: 1.1869e-05
Epoch 648/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 1.9719e-06 - val_loss: 1.6037e-06
Epoch 649/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 1.6896e-06 - val_loss: 1.6736e-06
Epoch 650/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 1.8430e-06 - val_loss: 1.3596e-06
Epoch 651/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 2.6486e-06 - val_loss: 3.9974e-06
Epoch 652/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 2.7020e-06 - val_loss: 2.4310e-06
Epoch 653/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 4.6078e-06 - val_loss: 1.4435e-06
Epoch 654/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 3.0852e-06 - val_loss: 1.4949e-06
Epoch 655/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 8.4255e-06 - val_loss: 1.8559e-05
Epoch 656/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 2.7205e-06 - val_loss: 1.3922e-06
Epoch 657/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 2.7953e-06 - val_loss: 2.4881e-06
Epoch 658/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 5.5690e-06 - val_loss: 1.5030e-06
Epoch 659/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 1.4997e-06 - val_loss: 1.5249e-06
Epoch 660/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 5.9972e-06 - val_loss: 2.1769e-06
Epoch 661/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 1.9981e-06 - val_loss: 4.6937e-06
Epoch 662/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 1.6219e-05 - val_loss: 2.3201e-06
Epoch 663/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 1.6286e-06 - val_loss: 1.4623e-06
Epoch 664/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 1.6818e-06 - val_loss: 9.8746e-06
Epoch 665/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 1.9951e-06 - val_loss: 2.0627e-06
Epoch 666/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 9.3081e-06 - val_loss: 8.1998e-06
Epoch 667/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 2.5016e-06 - val_loss: 2.1251e-06
Epoch 668/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 2.2423e-06 - val_loss: 3.9096e-06
Epoch 669/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 2.7451e-06 - val_loss: 1.5208e-06
Epoch 670/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 5.5929e-06 - val_loss: 1.7533e-06
Epoch 671/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 1.8447e-06 - val_loss: 5.1322e-05
Epoch 672/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 7.2189e-06 - val_loss: 1.9987e-06
Epoch 673/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 4.4331e-06 - val_loss: 1.6382e-06
Epoch 674/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 1.0510e-05 - val_loss: 1.7097e-05
Epoch 675/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 1.9443e-06 - val_loss: 1.5095e-06
Epoch 676/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 1.7940e-06 - val_loss: 1.9269e-06
Epoch 677/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 3.1738e-06 - val_loss: 1.5253e-06
Epoch 678/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 5.8129e-06 - val_loss: 3.6360e-06
Epoch 679/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 3.0350e-06 - val_loss: 1.4185e-06
Epoch 680/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 5.8244e-06 - val_loss: 2.9612e-06
Epoch 681/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 2.6790e-06 - val_loss: 5.9317e-06
Epoch 682/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 2.8968e-06 - val_loss: 1.3753e-06
Epoch 683/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 7.0388e-06 - val_loss: 1.2901e-05
Epoch 684/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 5.5089e-06 - val_loss: 1.5053e-06
Epoch 685/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 3.7561e-06 - val_loss: 1.9657e-05
Epoch 686/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 2.3843e-06 - val_loss: 1.5935e-06
Epoch 687/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 3.8617e-06 - val_loss: 1.2060e-06
Epoch 688/1000
3888/3888 [==============================] - 0s 106us/sample - loss: 3.2219e-06 - val_loss: 6.3618e-06
Epoch 689/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 8.8605e-06 - val_loss: 1.3222e-06
Epoch 690/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 2.3331e-06 - val_loss: 3.3300e-06
Epoch 691/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 1.6368e-06 - val_loss: 1.3297e-06
Epoch 692/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 4.8329e-06 - val_loss: 2.3770e-06
Epoch 693/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 2.8043e-06 - val_loss: 2.5965e-06
Epoch 694/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 6.9277e-06 - val_loss: 1.6307e-06
Epoch 695/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 2.6013e-06 - val_loss: 5.7026e-05
Epoch 696/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 4.3196e-06 - val_loss: 3.7107e-05
Epoch 697/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 2.9436e-06 - val_loss: 1.6274e-06
Epoch 698/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 9.2553e-06 - val_loss: 1.0876e-06
Epoch 699/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 1.6584e-06 - val_loss: 1.1857e-06
Epoch 700/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 4.2122e-06 - val_loss: 2.7331e-06
Epoch 701/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 1.1122e-05 - val_loss: 1.3649e-06
Epoch 702/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 1.4372e-06 - val_loss: 3.6740e-06
Epoch 703/1000
3888/3888 [==============================] - 0s 98us/sample - loss: 4.5740e-06 - val_loss: 1.4361e-06
Epoch 704/1000
3888/3888 [==============================] - 0s 98us/sample - loss: 1.3835e-06 - val_loss: 1.1924e-06
Epoch 705/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 5.9760e-06 - val_loss: 1.3695e-06
Epoch 706/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 1.7700e-06 - val_loss: 1.0775e-06
Epoch 707/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 2.1717e-06 - val_loss: 2.0667e-06
Epoch 708/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 6.3456e-06 - val_loss: 1.5766e-06
Epoch 709/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 2.9796e-06 - val_loss: 7.6243e-06
Epoch 710/1000
3888/3888 [==============================] - 0s 98us/sample - loss: 4.3017e-06 - val_loss: 1.3453e-06
Epoch 711/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 1.0637e-05 - val_loss: 1.0411e-06
Epoch 712/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 1.5320e-06 - val_loss: 2.1798e-06
Epoch 713/1000
3888/3888 [==============================] - 0s 98us/sample - loss: 1.8862e-06 - val_loss: 3.4081e-06
Epoch 714/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 6.3535e-06 - val_loss: 1.0466e-06
Epoch 715/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 1.3345e-06 - val_loss: 4.9182e-06
Epoch 716/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 2.7764e-06 - val_loss: 8.9795e-06
Epoch 717/1000
3888/3888 [==============================] - 0s 98us/sample - loss: 6.4241e-06 - val_loss: 1.9376e-06
Epoch 718/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 3.7059e-06 - val_loss: 2.8663e-06
Epoch 719/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 3.0772e-06 - val_loss: 1.0808e-05
Epoch 720/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 2.8220e-06 - val_loss: 1.1734e-06
Epoch 721/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 6.3519e-06 - val_loss: 1.1421e-06
Epoch 722/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 3.0362e-06 - val_loss: 6.7849e-06
Epoch 723/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 6.3880e-06 - val_loss: 1.1539e-06
Epoch 724/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 1.8311e-06 - val_loss: 1.6307e-06
Epoch 725/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 3.5614e-06 - val_loss: 1.5835e-06
Epoch 726/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 1.7161e-06 - val_loss: 1.4577e-06
Epoch 727/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 6.7547e-06 - val_loss: 1.2287e-06
Epoch 728/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 4.0986e-06 - val_loss: 1.0419e-06
Epoch 729/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 6.4966e-06 - val_loss: 1.0001e-06
Epoch 730/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 5.2007e-06 - val_loss: 1.4794e-05
Epoch 731/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 2.4366e-06 - val_loss: 1.0259e-06
Epoch 732/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 2.8042e-06 - val_loss: 6.0819e-06
Epoch 733/1000
3888/3888 [==============================] - 0s 106us/sample - loss: 6.6389e-06 - val_loss: 2.2165e-06
Epoch 734/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 1.6723e-06 - val_loss: 2.3272e-06
Epoch 735/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 3.6584e-06 - val_loss: 1.0546e-06
Epoch 736/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 4.9420e-06 - val_loss: 1.0166e-06
Epoch 737/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 3.1281e-06 - val_loss: 1.2723e-06
Epoch 738/1000
3888/3888 [==============================] - 0s 106us/sample - loss: 8.9904e-06 - val_loss: 1.2738e-06
Epoch 739/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 1.2107e-06 - val_loss: 1.0656e-06
Epoch 740/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 6.3808e-06 - val_loss: 2.1759e-06
Epoch 741/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 1.4318e-06 - val_loss: 1.2610e-06
Epoch 742/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 2.5476e-06 - val_loss: 2.1355e-06
Epoch 743/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 5.5866e-06 - val_loss: 3.5372e-06
Epoch 744/1000
3888/3888 [==============================] - 0s 106us/sample - loss: 3.9487e-06 - val_loss: 1.9419e-06
Epoch 745/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 5.2192e-06 - val_loss: 1.6982e-06
Epoch 746/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 4.3756e-06 - val_loss: 1.6649e-06
Epoch 747/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 2.0350e-06 - val_loss: 1.2832e-06
Epoch 748/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 1.5300e-05 - val_loss: 1.1413e-06
Epoch 749/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 1.7986e-06 - val_loss: 7.6191e-06
Epoch 750/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 1.3629e-06 - val_loss: 5.3663e-06
Epoch 751/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 1.9229e-06 - val_loss: 1.5807e-06
Epoch 752/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 2.1316e-06 - val_loss: 2.5694e-06
Epoch 753/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 3.5875e-06 - val_loss: 1.6416e-06
Epoch 754/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 5.6695e-06 - val_loss: 1.1779e-06
Epoch 755/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 1.9264e-06 - val_loss: 1.4852e-06
Epoch 756/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 4.1417e-06 - val_loss: 4.0825e-06
Epoch 757/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 1.9968e-06 - val_loss: 1.8138e-06
Epoch 758/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 6.2203e-06 - val_loss: 5.1587e-06
Epoch 759/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 3.7198e-06 - val_loss: 9.8413e-07
Epoch 760/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 5.9857e-06 - val_loss: 2.3127e-06
Epoch 761/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 2.7601e-06 - val_loss: 9.1543e-07
Epoch 762/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 7.0408e-06 - val_loss: 9.9067e-07
Epoch 763/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 2.8405e-06 - val_loss: 1.3683e-06
Epoch 764/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 2.0318e-06 - val_loss: 3.1831e-06
Epoch 765/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 7.4205e-06 - val_loss: 5.4743e-06
Epoch 766/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 2.1470e-06 - val_loss: 2.2197e-06
Epoch 767/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 2.8162e-06 - val_loss: 1.3075e-06
Epoch 768/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 5.3813e-06 - val_loss: 1.1984e-06
Epoch 769/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 3.1976e-06 - val_loss: 1.1190e-06
Epoch 770/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 1.7294e-06 - val_loss: 7.5016e-06
Epoch 771/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 3.1380e-06 - val_loss: 1.0088e-06
Epoch 772/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 9.6019e-06 - val_loss: 1.0890e-06
Epoch 773/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 2.6043e-06 - val_loss: 1.1233e-06
Epoch 774/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 1.7455e-06 - val_loss: 1.5507e-06
Epoch 775/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 2.7537e-06 - val_loss: 2.4299e-06
Epoch 776/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 6.9178e-06 - val_loss: 1.0166e-06
Epoch 777/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 2.2422e-06 - val_loss: 4.7166e-06
Epoch 778/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 6.3663e-06 - val_loss: 1.0086e-06
Epoch 779/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 1.8729e-06 - val_loss: 1.6734e-06
Epoch 780/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 6.6128e-06 - val_loss: 3.1822e-06
Epoch 781/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 1.9689e-06 - val_loss: 1.1789e-05
Epoch 782/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 4.5305e-06 - val_loss: 1.0456e-06
Epoch 783/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 3.5889e-06 - val_loss: 1.3464e-06
Epoch 784/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 2.8147e-06 - val_loss: 1.0526e-06
Epoch 785/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 6.6319e-06 - val_loss: 1.1504e-06
Epoch 786/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 1.4002e-06 - val_loss: 1.2570e-06
Epoch 787/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 1.4318e-05 - val_loss: 1.1011e-06
Epoch 788/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 1.0569e-06 - val_loss: 1.2097e-06
Epoch 789/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 2.5918e-06 - val_loss: 1.0921e-06
Epoch 790/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 1.4786e-06 - val_loss: 1.1854e-05
Epoch 791/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 4.3297e-06 - val_loss: 1.0324e-06
Epoch 792/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 6.7167e-06 - val_loss: 1.1346e-05
Epoch 793/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 1.9370e-06 - val_loss: 1.2745e-06
Epoch 794/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 3.7781e-06 - val_loss: 2.3013e-06
Epoch 795/1000
3888/3888 [==============================] - 0s 106us/sample - loss: 1.3460e-06 - val_loss: 1.2996e-06
Epoch 796/1000
3888/3888 [==============================] - 0s 106us/sample - loss: 2.2860e-06 - val_loss: 7.4163e-06
Epoch 797/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 8.1239e-06 - val_loss: 1.4879e-05
Epoch 798/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 1.5468e-06 - val_loss: 1.0941e-06
Epoch 799/1000
3888/3888 [==============================] - 0s 106us/sample - loss: 2.1892e-06 - val_loss: 6.7516e-06
Epoch 800/1000
3888/3888 [==============================] - 0s 106us/sample - loss: 7.4627e-06 - val_loss: 1.2901e-06
Epoch 801/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 2.0532e-06 - val_loss: 1.4233e-05
Epoch 802/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 6.7566e-06 - val_loss: 1.5241e-06
Epoch 803/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 3.2775e-06 - val_loss: 1.0330e-06
Epoch 804/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 1.2983e-06 - val_loss: 3.0350e-06
Epoch 805/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 4.8705e-06 - val_loss: 1.9995e-06
Epoch 806/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 2.1710e-06 - val_loss: 4.7666e-06
Epoch 807/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 5.2751e-06 - val_loss: 1.3372e-06
Epoch 808/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 4.9725e-06 - val_loss: 1.3906e-06
Epoch 809/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 6.0348e-06 - val_loss: 4.1727e-06
Epoch 810/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 2.1942e-06 - val_loss: 9.9325e-07
Epoch 811/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 1.8222e-06 - val_loss: 1.2197e-06
Epoch 812/1000
3888/3888 [==============================] - 0s 108us/sample - loss: 9.1813e-06 - val_loss: 1.3247e-06
Epoch 813/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 1.4130e-06 - val_loss: 1.0631e-06
Epoch 814/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 3.1664e-06 - val_loss: 1.1733e-06
Epoch 815/1000
3888/3888 [==============================] - 0s 108us/sample - loss: 3.6440e-06 - val_loss: 1.4741e-06
Epoch 816/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 2.5945e-06 - val_loss: 2.5588e-06
Epoch 817/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 3.8969e-06 - val_loss: 5.0881e-06
Epoch 818/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 4.0909e-06 - val_loss: 1.2971e-05
Epoch 819/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 3.6708e-06 - val_loss: 1.1811e-06
Epoch 820/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 9.0817e-06 - val_loss: 9.7526e-07
Epoch 821/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 3.1499e-06 - val_loss: 4.6765e-05
Epoch 822/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 3.9781e-06 - val_loss: 3.1292e-06
Epoch 823/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 1.9257e-06 - val_loss: 1.1358e-06
Epoch 824/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 1.6832e-06 - val_loss: 2.2892e-06
Epoch 825/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 1.0017e-05 - val_loss: 9.8985e-07
Epoch 826/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 1.9602e-06 - val_loss: 9.7654e-07
Epoch 827/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 5.5129e-06 - val_loss: 2.7151e-06
Epoch 828/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 1.5211e-06 - val_loss: 3.0591e-06
Epoch 829/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 2.1787e-06 - val_loss: 5.4666e-05
Epoch 830/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 6.0702e-06 - val_loss: 3.6713e-06
Epoch 831/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 1.9802e-06 - val_loss: 2.7535e-06
Epoch 832/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 3.9714e-06 - val_loss: 1.0601e-06
Epoch 833/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 2.0874e-06 - val_loss: 1.4974e-06
Epoch 834/1000
3888/3888 [==============================] - 0s 100us/sample - loss: 4.6706e-06 - val_loss: 2.1163e-06
Epoch 835/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 6.9537e-06 - val_loss: 1.2902e-06
Epoch 836/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 1.0588e-06 - val_loss: 2.5100e-06
Epoch 837/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 5.0577e-06 - val_loss: 1.1658e-06
Epoch 838/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 2.1714e-06 - val_loss: 1.0455e-06
Epoch 839/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 8.7025e-06 - val_loss: 1.9254e-05
Epoch 840/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 1.8381e-06 - val_loss: 2.4443e-06
Epoch 841/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 2.7220e-06 - val_loss: 1.4713e-06
Epoch 842/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 4.3758e-06 - val_loss: 9.2539e-07
Epoch 843/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 1.7809e-06 - val_loss: 7.9680e-06
Epoch 844/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 4.0015e-06 - val_loss: 4.2171e-06
Epoch 845/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 5.8430e-06 - val_loss: 2.2721e-05
Epoch 846/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 6.0770e-06 - val_loss: 1.0480e-06
Epoch 847/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 1.2198e-06 - val_loss: 1.3538e-06
Epoch 848/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 3.1428e-06 - val_loss: 1.3783e-06
Epoch 849/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 9.0912e-06 - val_loss: 2.5423e-06
Epoch 850/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 2.3514e-06 - val_loss: 6.5171e-06
Epoch 851/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 7.6661e-06 - val_loss: 3.4803e-06
Epoch 852/1000
3888/3888 [==============================] - 0s 102us/sample - loss: 1.8125e-06 - val_loss: 9.5794e-07
Epoch 853/1000
3888/3888 [==============================] - 0s 103us/sample - loss: 1.7280e-06 - val_loss: 1.3860e-06
Epoch 854/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 3.3451e-06 - val_loss: 1.4447e-06
Epoch 855/1000
3888/3888 [==============================] - 0s 106us/sample - loss: 3.2464e-06 - val_loss: 1.7784e-06
Epoch 856/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 3.2174e-06 - val_loss: 9.8018e-06
Epoch 857/1000
3888/3888 [==============================] - ETA: 0s - loss: 4.7744e-0 - 0s 105us/sample - loss: 4.0797e-06 - val_loss: 1.1600e-06
Epoch 858/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 5.0555e-06 - val_loss: 3.4396e-06
Epoch 859/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 6.4839e-06 - val_loss: 2.0227e-05
Epoch 860/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 1.6769e-06 - val_loss: 1.4328e-05
Epoch 861/1000
3200/3888 [=======================>......] - ETA: 0s - loss: 3.2997e-06Restoring model weights from the end of the best epoch.
3888/3888 [==============================] - 0s 103us/sample - loss: 3.0834e-06 - val_loss: 6.3939e-06
Epoch 00861: early stopping
In [32]:
print(history.history.keys())
print('best value: ', autoencoder.evaluate(X_train_1D_norm, X_train_1D_norm, verbose=0))


pd.DataFrame(history.history).plot(figsize=(8, 5), logy=True)
plt.grid()
dict_keys(['loss', 'val_loss'])
best value:  9.154292058142889e-07
In [33]:
X_reconstructions = autoencoder.predict(X_train_1D_norm)
X_reconstructions = stdscaler.inverse_transform(X_reconstructions)
In [34]:
calculateerror(X_train_1D.reshape(len(times),len(groups),nl,nc), 
               X_reconstructions.reshape(len(times),len(groups),nl,nc), 
               groups,
               print_step=0)
max_abs_error:  14.3992919921875
mean_abs_error:  0.02822514491985846
/home/viluiz/anaconda3/envs/py3ml/lib/python3.7/site-packages/ipykernel_launcher.py:3: RuntimeWarning: divide by zero encountered in true_divide
  This is separate from the ipykernel package so we can avoid doing imports until
In [35]:
fig, ax = plt.subplots(2,4, figsize=[20,10])
for i, group in enumerate(groups):
    im = ax.flatten()[i].imshow(X_reconstructions.reshape(len(times),len(groups),nl,nc)[100,i,:,:])
    fig.colorbar(im, ax=ax.flatten()[i])
    ax.flatten()[i].set_title(group)
In [36]:
fig, ax = plt.subplots(2,4, figsize=[20,10])
for i, group in enumerate(groups):
    ax.flatten()[i].plot(times, X_train_1D[:,i*nl*nc+4])
    ax.flatten()[i].plot(times, X_reconstructions[:,i*nl*nc+4],'--')
    ax.flatten()[i].set_title(group)
In [37]:
np.random.seed(42)
tf.random.set_seed(42)

# Need to have validation loss
early_stopping = keras.callbacks.EarlyStopping(monitor='val_loss',
                                               min_delta=0.0,
                                               patience=100,
                                               verbose=2,
                                               restore_best_weights=True)

encoder = keras.models.Sequential([keras.layers.Dense(200, input_shape=[800]),
                                   keras.layers.Dense(15)])
decoder = keras.models.Sequential([keras.layers.Dense(200, input_shape=[15]),
                                   keras.layers.Dense(800),
                                  ])
autoencoder = keras.models.Sequential([encoder, decoder])

autoencoder.compile(loss="mse", 
                    optimizer=keras.optimizers.Nadam(lr=0.0003, beta_1=0.9, beta_2=0.999)
                    )
encoder.summary()
decoder.summary()
Model: "sequential_3"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_2 (Dense)              (None, 200)               160200    
_________________________________________________________________
dense_3 (Dense)              (None, 15)                3015      
=================================================================
Total params: 163,215
Trainable params: 163,215
Non-trainable params: 0
_________________________________________________________________
Model: "sequential_4"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_4 (Dense)              (None, 200)               3200      
_________________________________________________________________
dense_5 (Dense)              (None, 800)               160800    
=================================================================
Total params: 164,000
Trainable params: 164,000
Non-trainable params: 0
_________________________________________________________________
In [38]:
history = autoencoder.fit(X_train_1D_norm, 
                          X_train_1D_norm, 
                          epochs=1000,
                          validation_data=(X_train_1D_norm, X_train_1D_norm),
                          callbacks=[early_stopping])
Train on 3888 samples, validate on 3888 samples
Epoch 1/1000
3888/3888 [==============================] - 1s 335us/sample - loss: 0.0535 - val_loss: 0.0152
Epoch 2/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 0.0083 - val_loss: 0.0040
Epoch 3/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 0.0029 - val_loss: 0.0019
Epoch 4/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 0.0019 - val_loss: 0.0017
Epoch 5/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 0.0018 - val_loss: 0.0019
Epoch 6/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 0.0016 - val_loss: 0.0012
Epoch 7/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 0.0013 - val_loss: 7.5517e-04
Epoch 8/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 5.8562e-04 - val_loss: 4.4975e-04
Epoch 9/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 4.9152e-04 - val_loss: 3.6823e-04
Epoch 10/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 3.6893e-04 - val_loss: 1.9823e-04
Epoch 11/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 2.7908e-04 - val_loss: 1.0087e-04
Epoch 12/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 2.6507e-04 - val_loss: 7.1798e-05
Epoch 13/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 1.0696e-04 - val_loss: 6.1843e-05
Epoch 14/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 2.4705e-04 - val_loss: 4.1281e-05
Epoch 15/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 6.1790e-05 - val_loss: 4.8645e-05
Epoch 16/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 3.1749e-04 - val_loss: 7.6316e-05
Epoch 17/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 5.7837e-05 - val_loss: 3.9668e-05
Epoch 18/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 4.6528e-05 - val_loss: 3.8870e-05
Epoch 19/1000
3888/3888 [==============================] - 1s 182us/sample - loss: 3.4340e-04 - val_loss: 5.0968e-05
Epoch 20/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 1.3672e-04 - val_loss: 3.5644e-05
Epoch 21/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 8.9512e-05 - val_loss: 3.8801e-05
Epoch 22/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 4.8148e-05 - val_loss: 3.9136e-05
Epoch 23/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 3.3418e-04 - val_loss: 3.5500e-05
Epoch 24/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 4.4832e-05 - val_loss: 5.4451e-05
Epoch 25/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 1.3332e-04 - val_loss: 4.1293e-05
Epoch 26/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 1.3092e-04 - val_loss: 4.5489e-05
Epoch 27/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 5.1948e-05 - val_loss: 4.6868e-05
Epoch 28/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 2.2867e-04 - val_loss: 1.0597e-04
Epoch 29/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 4.4215e-05 - val_loss: 4.1156e-05
Epoch 30/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 1.2528e-04 - val_loss: 9.1212e-04
Epoch 31/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 7.5877e-05 - val_loss: 3.5619e-05
Epoch 32/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 7.0622e-05 - val_loss: 9.7798e-05
Epoch 33/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 3.2486e-04 - val_loss: 3.8138e-05
Epoch 34/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 4.1571e-05 - val_loss: 3.9389e-05
Epoch 35/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 1.4383e-04 - val_loss: 3.6089e-05
Epoch 36/1000
3888/3888 [==============================] - 1s 186us/sample - loss: 6.6203e-05 - val_loss: 3.4543e-05
Epoch 37/1000
3888/3888 [==============================] - 1s 179us/sample - loss: 7.5243e-05 - val_loss: 3.5585e-05
Epoch 38/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 5.3659e-05 - val_loss: 3.7065e-05
Epoch 39/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 4.9899e-05 - val_loss: 8.9075e-05
Epoch 40/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 1.8070e-04 - val_loss: 3.7009e-05
Epoch 41/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 7.0889e-05 - val_loss: 4.4309e-05
Epoch 42/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 1.9950e-04 - val_loss: 0.0023
Epoch 43/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 1.7480e-04 - val_loss: 3.2982e-05
Epoch 44/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 3.3070e-05 - val_loss: 4.2519e-05
Epoch 45/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 3.6713e-05 - val_loss: 6.0048e-05
Epoch 46/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 1.0660e-04 - val_loss: 3.1515e-05
Epoch 47/1000
3888/3888 [==============================] - 1s 178us/sample - loss: 1.2796e-04 - val_loss: 3.8759e-05
Epoch 48/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 3.9431e-05 - val_loss: 5.1361e-05
Epoch 49/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 8.8270e-05 - val_loss: 3.0570e-05
Epoch 50/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 3.7611e-05 - val_loss: 5.8796e-05
Epoch 51/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 4.7246e-05 - val_loss: 3.0547e-05
Epoch 52/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 7.4366e-05 - val_loss: 3.2117e-05
Epoch 53/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 7.5940e-05 - val_loss: 5.6891e-05
Epoch 54/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 7.7723e-05 - val_loss: 4.0344e-05
Epoch 55/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 8.5169e-05 - val_loss: 2.2869e-05
Epoch 56/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 3.4925e-05 - val_loss: 2.0909e-04
Epoch 57/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 1.5720e-04 - val_loss: 2.5054e-05
Epoch 58/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 4.5872e-05 - val_loss: 0.0011
Epoch 59/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 5.3317e-05 - val_loss: 2.0139e-05
Epoch 60/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 3.2335e-05 - val_loss: 2.0200e-05
Epoch 61/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 4.2515e-05 - val_loss: 2.4973e-05
Epoch 62/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 8.6700e-05 - val_loss: 2.7531e-05
Epoch 63/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 1.9338e-05 - val_loss: 1.9466e-05
Epoch 64/1000
3888/3888 [==============================] - 1s 180us/sample - loss: 4.6554e-05 - val_loss: 2.1961e-05
Epoch 65/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 5.4244e-05 - val_loss: 4.8934e-05
Epoch 66/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 5.2925e-05 - val_loss: 1.8760e-05
Epoch 67/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 1.3063e-04 - val_loss: 8.5756e-05
Epoch 68/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 1.7991e-05 - val_loss: 1.6724e-05
Epoch 69/1000
3888/3888 [==============================] - 1s 187us/sample - loss: 5.3857e-05 - val_loss: 2.0216e-05
Epoch 70/1000
3888/3888 [==============================] - 1s 183us/sample - loss: 4.5070e-05 - val_loss: 1.5495e-05
Epoch 71/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 1.6068e-05 - val_loss: 1.5344e-05
Epoch 72/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 8.5351e-05 - val_loss: 1.9476e-05
Epoch 73/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 4.6672e-05 - val_loss: 1.6797e-05
Epoch 74/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 1.6412e-05 - val_loss: 1.5885e-05
Epoch 75/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 4.5536e-05 - val_loss: 1.8104e-05
Epoch 76/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 1.8506e-05 - val_loss: 1.6382e-05
Epoch 77/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 7.5506e-05 - val_loss: 1.4393e-05
Epoch 78/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 3.6173e-05 - val_loss: 1.3941e-05
Epoch 79/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 2.3019e-05 - val_loss: 0.0020
Epoch 80/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 1.5053e-04 - val_loss: 1.4039e-05
Epoch 81/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 1.7747e-05 - val_loss: 1.2500e-05
Epoch 82/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 3.1950e-05 - val_loss: 1.3004e-05
Epoch 83/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 4.2864e-05 - val_loss: 1.2150e-05
Epoch 84/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 1.2116e-05 - val_loss: 1.1974e-05
Epoch 85/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 3.9654e-05 - val_loss: 1.3497e-05
Epoch 86/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 3.9995e-05 - val_loss: 1.1237e-05
Epoch 87/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 3.0470e-05 - val_loss: 1.4938e-05
Epoch 88/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 3.1920e-05 - val_loss: 5.1851e-04
Epoch 89/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 3.8859e-05 - val_loss: 9.0726e-06
Epoch 90/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 3.0550e-05 - val_loss: 2.3195e-05
Epoch 91/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 1.5695e-05 - val_loss: 8.0660e-06
Epoch 92/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 1.1090e-05 - val_loss: 5.1020e-05
Epoch 93/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 6.0481e-05 - val_loss: 7.8439e-06
Epoch 94/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 8.8006e-06 - val_loss: 9.1139e-05
Epoch 95/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 9.9224e-05 - val_loss: 8.4015e-06
Epoch 96/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 7.7083e-06 - val_loss: 8.9406e-06
Epoch 97/1000
3888/3888 [==============================] - 1s 178us/sample - loss: 8.9340e-06 - val_loss: 1.2880e-05
Epoch 98/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 4.9769e-05 - val_loss: 8.0103e-06
Epoch 99/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 2.2969e-05 - val_loss: 8.7050e-06
Epoch 100/1000
3888/3888 [==============================] - 1s 178us/sample - loss: 8.7083e-06 - val_loss: 8.9494e-06
Epoch 101/1000
3888/3888 [==============================] - 1s 182us/sample - loss: 1.7718e-05 - val_loss: 2.2135e-05
Epoch 102/1000
3888/3888 [==============================] - 1s 183us/sample - loss: 3.7573e-05 - val_loss: 6.2436e-06
Epoch 103/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 7.1693e-06 - val_loss: 7.1479e-06
Epoch 104/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 2.6742e-05 - val_loss: 1.6195e-05
Epoch 105/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 1.7330e-05 - val_loss: 1.3771e-05
Epoch 106/1000
3888/3888 [==============================] - 1s 180us/sample - loss: 6.6983e-05 - val_loss: 1.2962e-05
Epoch 107/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 6.2605e-06 - val_loss: 5.8326e-06
Epoch 108/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 9.1957e-06 - val_loss: 6.8343e-06
Epoch 109/1000
3888/3888 [==============================] - 1s 184us/sample - loss: 2.9360e-05 - val_loss: 5.9915e-06
Epoch 110/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 6.9871e-05 - val_loss: 3.8160e-05
Epoch 111/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 1.4912e-05 - val_loss: 6.0910e-06
Epoch 112/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 5.8179e-06 - val_loss: 6.3737e-06
Epoch 113/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 2.1265e-05 - val_loss: 3.2333e-05
Epoch 114/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 1.1676e-05 - val_loss: 1.0195e-05
Epoch 115/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 2.6807e-05 - val_loss: 5.6441e-06
Epoch 116/1000
3888/3888 [==============================] - 1s 179us/sample - loss: 4.4121e-05 - val_loss: 2.3282e-04
Epoch 117/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 1.4914e-05 - val_loss: 5.2071e-06
Epoch 118/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 2.2556e-05 - val_loss: 5.1676e-06
Epoch 119/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 7.4886e-06 - val_loss: 8.5413e-06
Epoch 120/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 2.2591e-05 - val_loss: 6.2924e-06
Epoch 121/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 9.2851e-06 - val_loss: 2.4171e-04
Epoch 122/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 3.9253e-05 - val_loss: 5.4999e-06
Epoch 123/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 6.3465e-06 - val_loss: 5.3613e-06
Epoch 124/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 3.5078e-05 - val_loss: 5.4000e-06
Epoch 125/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 5.7069e-06 - val_loss: 5.3014e-06
Epoch 126/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 5.7828e-05 - val_loss: 5.2466e-06
Epoch 127/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 5.6004e-06 - val_loss: 5.9656e-05
Epoch 128/1000
3888/3888 [==============================] - 1s 179us/sample - loss: 1.7135e-05 - val_loss: 4.9637e-06
Epoch 129/1000
3888/3888 [==============================] - 1s 180us/sample - loss: 2.4982e-05 - val_loss: 2.3459e-05
Epoch 130/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 7.3666e-06 - val_loss: 6.2106e-06
Epoch 131/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 3.0896e-05 - val_loss: 5.5841e-06
Epoch 132/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 1.0555e-05 - val_loss: 9.0032e-06
Epoch 133/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 2.4195e-05 - val_loss: 1.2829e-04
Epoch 134/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 2.6516e-05 - val_loss: 5.1498e-06
Epoch 135/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 5.2393e-06 - val_loss: 8.0525e-06
Epoch 136/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 2.5683e-05 - val_loss: 1.1304e-05
Epoch 137/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 1.1194e-05 - val_loss: 1.0511e-04
Epoch 138/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 7.0561e-05 - val_loss: 5.7109e-06
Epoch 139/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 6.4951e-06 - val_loss: 1.3218e-05
Epoch 140/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 9.8751e-06 - val_loss: 7.0158e-06
Epoch 141/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 2.3423e-05 - val_loss: 5.6475e-05
Epoch 142/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 9.1158e-06 - val_loss: 5.5077e-06
Epoch 143/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 1.2764e-05 - val_loss: 4.9976e-06
Epoch 144/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 1.2453e-05 - val_loss: 8.4360e-06
Epoch 145/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 3.0160e-05 - val_loss: 4.5491e-06
Epoch 146/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 7.5740e-06 - val_loss: 4.5006e-06
Epoch 147/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 3.7311e-05 - val_loss: 5.0322e-06
Epoch 148/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 2.3695e-05 - val_loss: 3.8241e-05
Epoch 149/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 1.1614e-05 - val_loss: 5.3102e-06
Epoch 150/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 1.0683e-05 - val_loss: 9.5388e-06
Epoch 151/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 3.6025e-05 - val_loss: 1.0934e-04
Epoch 152/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 1.3098e-05 - val_loss: 7.0422e-06
Epoch 153/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 1.0359e-05 - val_loss: 5.0111e-06
Epoch 154/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 9.8739e-06 - val_loss: 5.0948e-06
Epoch 155/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 4.4709e-05 - val_loss: 4.4007e-06
Epoch 156/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 7.8001e-06 - val_loss: 3.3455e-05
Epoch 157/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 1.7936e-05 - val_loss: 4.1190e-06
Epoch 158/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 6.4802e-06 - val_loss: 6.3437e-06
Epoch 159/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 1.3475e-05 - val_loss: 1.2931e-05
Epoch 160/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 1.6131e-05 - val_loss: 3.2522e-05
Epoch 161/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 2.7984e-05 - val_loss: 6.7982e-06
Epoch 162/1000
3888/3888 [==============================] - 1s 178us/sample - loss: 8.5168e-06 - val_loss: 4.5795e-06
Epoch 163/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 7.4001e-06 - val_loss: 4.9758e-06
Epoch 164/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 4.2477e-05 - val_loss: 4.3723e-06
Epoch 165/1000
3888/3888 [==============================] - 1s 180us/sample - loss: 4.6957e-06 - val_loss: 2.1116e-05
Epoch 166/1000
3888/3888 [==============================] - 1s 178us/sample - loss: 3.3955e-05 - val_loss: 4.0128e-06
Epoch 167/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 4.1741e-06 - val_loss: 8.2640e-06
Epoch 168/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 1.3437e-05 - val_loss: 4.7008e-06
Epoch 169/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 5.2707e-06 - val_loss: 1.0479e-05
Epoch 170/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 2.1098e-05 - val_loss: 6.0238e-05
Epoch 171/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 1.5370e-05 - val_loss: 4.2272e-06
Epoch 172/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 2.5175e-05 - val_loss: 2.4935e-05
Epoch 173/1000
3888/3888 [==============================] - 1s 182us/sample - loss: 9.5772e-06 - val_loss: 4.1929e-06
Epoch 174/1000
3888/3888 [==============================] - 1s 181us/sample - loss: 7.8287e-06 - val_loss: 2.0541e-05
Epoch 175/1000
3888/3888 [==============================] - 1s 179us/sample - loss: 3.3390e-05 - val_loss: 3.8244e-05
Epoch 176/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 5.3276e-06 - val_loss: 1.2407e-05
Epoch 177/1000
3888/3888 [==============================] - 1s 178us/sample - loss: 3.6688e-05 - val_loss: 5.6262e-06
Epoch 178/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 5.1916e-06 - val_loss: 4.3469e-06
Epoch 179/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 8.9968e-06 - val_loss: 4.9098e-06
Epoch 180/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 2.4352e-05 - val_loss: 5.6497e-06
Epoch 181/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 4.2309e-06 - val_loss: 5.1506e-06
Epoch 182/1000
3888/3888 [==============================] - 1s 178us/sample - loss: 7.8824e-06 - val_loss: 3.6288e-06
Epoch 183/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 7.3779e-06 - val_loss: 8.7545e-06
Epoch 184/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 3.9285e-05 - val_loss: 3.4431e-06
Epoch 185/1000
3888/3888 [==============================] - 1s 179us/sample - loss: 3.9090e-06 - val_loss: 2.6903e-05
Epoch 186/1000
3888/3888 [==============================] - 1s 178us/sample - loss: 1.0726e-05 - val_loss: 7.9060e-06
Epoch 187/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 1.3175e-05 - val_loss: 4.2922e-06
Epoch 188/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 2.8306e-05 - val_loss: 3.9385e-06
Epoch 189/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 1.1134e-05 - val_loss: 4.0826e-06
Epoch 190/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 8.9324e-06 - val_loss: 1.5944e-04
Epoch 191/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 3.0891e-05 - val_loss: 3.2172e-06
Epoch 192/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 3.9906e-06 - val_loss: 4.1166e-06
Epoch 193/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 1.3886e-05 - val_loss: 4.9027e-06
Epoch 194/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 1.8207e-05 - val_loss: 3.5072e-06
Epoch 195/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 4.4939e-06 - val_loss: 3.5884e-06
Epoch 196/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 1.5110e-05 - val_loss: 5.6683e-06
Epoch 197/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 2.6174e-05 - val_loss: 3.4820e-06
Epoch 198/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 6.7733e-06 - val_loss: 6.7507e-05
Epoch 199/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 1.8176e-05 - val_loss: 5.0390e-06
Epoch 200/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 6.9197e-06 - val_loss: 3.7241e-06
Epoch 201/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 1.2783e-05 - val_loss: 1.7701e-05
Epoch 202/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 5.3542e-06 - val_loss: 3.5766e-06
Epoch 203/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 4.4166e-05 - val_loss: 4.8503e-06
Epoch 204/1000
3888/3888 [==============================] - 1s 178us/sample - loss: 4.4666e-06 - val_loss: 3.3946e-06
Epoch 205/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 8.5782e-06 - val_loss: 3.8985e-06
Epoch 206/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 7.7951e-06 - val_loss: 5.4282e-06
Epoch 207/1000
3888/3888 [==============================] - 1s 180us/sample - loss: 1.7579e-05 - val_loss: 7.0563e-06
Epoch 208/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 8.9295e-06 - val_loss: 4.7452e-06
Epoch 209/1000
3888/3888 [==============================] - 1s 181us/sample - loss: 1.2439e-05 - val_loss: 3.6179e-06
Epoch 210/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 4.7511e-06 - val_loss: 5.4727e-06
Epoch 211/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 2.7974e-05 - val_loss: 2.6158e-05
Epoch 212/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 1.5980e-05 - val_loss: 3.7714e-06
Epoch 213/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 5.4338e-06 - val_loss: 3.5042e-06
Epoch 214/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 1.4549e-05 - val_loss: 3.9779e-06
Epoch 215/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 5.7172e-06 - val_loss: 6.5461e-06
Epoch 216/1000
3888/3888 [==============================] - 1s 179us/sample - loss: 3.4467e-05 - val_loss: 3.2974e-06
Epoch 217/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 8.1501e-06 - val_loss: 3.6679e-06
Epoch 218/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 4.2534e-06 - val_loss: 3.3477e-06
Epoch 219/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 7.9117e-05 - val_loss: 3.4949e-05
Epoch 220/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 2.7009e-05 - val_loss: 5.0496e-06
Epoch 221/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 3.7575e-06 - val_loss: 3.3998e-06
Epoch 222/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 3.6536e-06 - val_loss: 4.7240e-06
Epoch 223/1000
3888/3888 [==============================] - 1s 179us/sample - loss: 1.3516e-05 - val_loss: 3.8390e-06
Epoch 224/1000
3888/3888 [==============================] - 1s 186us/sample - loss: 3.4694e-06 - val_loss: 3.2072e-06
Epoch 225/1000
3888/3888 [==============================] - 1s 182us/sample - loss: 1.0708e-05 - val_loss: 3.3626e-06
Epoch 226/1000
3888/3888 [==============================] - 1s 187us/sample - loss: 8.0721e-06 - val_loss: 3.9620e-06
Epoch 227/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 7.0289e-06 - val_loss: 3.2581e-06
Epoch 228/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 1.9292e-05 - val_loss: 7.3522e-06
Epoch 229/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 9.6425e-06 - val_loss: 1.3922e-05
Epoch 230/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 4.9744e-06 - val_loss: 4.8593e-06
Epoch 231/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 1.0969e-05 - val_loss: 1.8333e-05
Epoch 232/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 2.9244e-05 - val_loss: 3.0772e-06
Epoch 233/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 3.9642e-06 - val_loss: 5.8316e-06
Epoch 234/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 6.9073e-06 - val_loss: 4.0091e-06
Epoch 235/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 4.4149e-06 - val_loss: 5.1649e-06
Epoch 236/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 2.9171e-05 - val_loss: 4.0765e-05
Epoch 237/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 1.1365e-05 - val_loss: 2.9481e-06
Epoch 238/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 8.3525e-06 - val_loss: 6.1301e-05
Epoch 239/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 1.5545e-05 - val_loss: 4.8968e-05
Epoch 240/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 1.3042e-05 - val_loss: 2.7705e-06
Epoch 241/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 6.8420e-06 - val_loss: 2.1102e-05
Epoch 242/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 2.2589e-05 - val_loss: 1.0970e-04
Epoch 243/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 6.3135e-06 - val_loss: 3.6154e-06
Epoch 244/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 5.4153e-06 - val_loss: 2.9603e-06
Epoch 245/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 2.2659e-05 - val_loss: 3.3719e-06
Epoch 246/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 2.7925e-05 - val_loss: 5.1221e-06
Epoch 247/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 3.0594e-06 - val_loss: 2.8336e-06
Epoch 248/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 1.4678e-05 - val_loss: 4.5219e-06
Epoch 249/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 8.3124e-06 - val_loss: 6.9377e-06
Epoch 250/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 1.0095e-05 - val_loss: 3.0813e-06
Epoch 251/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 8.2342e-06 - val_loss: 3.0500e-06
Epoch 252/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 3.1783e-06 - val_loss: 2.7052e-06
Epoch 253/1000
3888/3888 [==============================] - 1s 183us/sample - loss: 3.7919e-05 - val_loss: 3.1820e-06
Epoch 254/1000
3888/3888 [==============================] - 1s 191us/sample - loss: 3.0671e-06 - val_loss: 1.3160e-05
Epoch 255/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 7.3599e-06 - val_loss: 1.1802e-05
Epoch 256/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 1.0280e-05 - val_loss: 7.9171e-05
Epoch 257/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 1.4619e-05 - val_loss: 3.1317e-06
Epoch 258/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 1.0080e-05 - val_loss: 3.6632e-06
Epoch 259/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 6.5901e-06 - val_loss: 1.3614e-05
Epoch 260/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 1.3085e-05 - val_loss: 2.4088e-06
Epoch 261/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 5.5287e-06 - val_loss: 2.9948e-06
Epoch 262/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 1.7372e-05 - val_loss: 2.8046e-06
Epoch 263/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 5.6635e-06 - val_loss: 2.7423e-06
Epoch 264/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 1.7643e-05 - val_loss: 5.3140e-06
Epoch 265/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 1.1403e-05 - val_loss: 2.6308e-06
Epoch 266/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 1.1165e-05 - val_loss: 2.8155e-06
Epoch 267/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 1.0328e-05 - val_loss: 6.9861e-05
Epoch 268/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 3.0866e-05 - val_loss: 4.2737e-06
Epoch 269/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 2.6277e-06 - val_loss: 4.7183e-06
Epoch 270/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 6.5881e-06 - val_loss: 5.0018e-06
Epoch 271/1000
3888/3888 [==============================] - 1s 182us/sample - loss: 2.5373e-05 - val_loss: 2.4953e-06
Epoch 272/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 2.5949e-06 - val_loss: 6.7575e-06
Epoch 273/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 5.4496e-06 - val_loss: 9.0777e-05
Epoch 274/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 1.0716e-05 - val_loss: 7.0088e-06
Epoch 275/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 6.1252e-06 - val_loss: 7.9399e-06
Epoch 276/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 1.2819e-05 - val_loss: 1.1203e-05
Epoch 277/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 1.4371e-05 - val_loss: 4.4543e-06
Epoch 278/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 1.2190e-05 - val_loss: 2.8272e-06
Epoch 279/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 4.1505e-06 - val_loss: 4.8731e-06
Epoch 280/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 2.8524e-05 - val_loss: 4.0713e-06
Epoch 281/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 3.4387e-06 - val_loss: 2.7308e-06
Epoch 282/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 4.2541e-06 - val_loss: 2.6044e-06
Epoch 283/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 1.1921e-05 - val_loss: 7.5142e-04
Epoch 284/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 3.0587e-05 - val_loss: 2.9926e-06
Epoch 285/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 2.5475e-06 - val_loss: 2.3648e-06
Epoch 286/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 2.5947e-06 - val_loss: 4.1283e-06
Epoch 287/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 6.9303e-06 - val_loss: 2.4575e-06
Epoch 288/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 1.8683e-05 - val_loss: 3.0071e-06
Epoch 289/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 8.4518e-06 - val_loss: 8.3101e-06
Epoch 290/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 5.8345e-06 - val_loss: 4.1932e-06
Epoch 291/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 1.4498e-05 - val_loss: 3.2619e-06
Epoch 292/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 6.6530e-06 - val_loss: 1.9060e-05
Epoch 293/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 1.0283e-05 - val_loss: 3.6969e-04
Epoch 294/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 1.8000e-05 - val_loss: 7.3509e-06
Epoch 295/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 5.1389e-06 - val_loss: 3.0402e-06
Epoch 296/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 5.8336e-06 - val_loss: 1.7918e-05
Epoch 297/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 2.1507e-05 - val_loss: 1.3124e-05
Epoch 298/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 5.7472e-06 - val_loss: 1.4062e-05
Epoch 299/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 2.0254e-05 - val_loss: 3.7220e-06
Epoch 300/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 2.4374e-06 - val_loss: 2.3589e-06
Epoch 301/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 5.0143e-06 - val_loss: 2.9043e-05
Epoch 302/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 3.7488e-05 - val_loss: 2.6786e-06
Epoch 303/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 2.5928e-06 - val_loss: 3.9229e-06
Epoch 304/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 1.0839e-05 - val_loss: 2.4523e-06
Epoch 305/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 3.1102e-06 - val_loss: 2.1836e-06
Epoch 306/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 5.9010e-06 - val_loss: 8.9886e-06
Epoch 307/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 8.4445e-06 - val_loss: 2.3263e-06
Epoch 308/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 1.6847e-05 - val_loss: 2.3667e-06
Epoch 309/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 1.0421e-05 - val_loss: 8.4373e-05
Epoch 310/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 6.3871e-06 - val_loss: 3.5441e-06
Epoch 311/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 4.0376e-05 - val_loss: 1.5477e-05
Epoch 312/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 3.2257e-06 - val_loss: 2.0410e-06
Epoch 313/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 7.8253e-06 - val_loss: 2.9242e-06
Epoch 314/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 2.3067e-06 - val_loss: 2.1962e-06
Epoch 315/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 1.8522e-05 - val_loss: 2.0109e-05
Epoch 316/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 3.7619e-06 - val_loss: 2.1300e-06
Epoch 317/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 6.8799e-06 - val_loss: 7.6182e-06
Epoch 318/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 8.5783e-06 - val_loss: 2.3599e-06
Epoch 319/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 7.3191e-06 - val_loss: 6.5161e-06
Epoch 320/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 7.6015e-06 - val_loss: 2.1173e-06
Epoch 321/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 8.1310e-06 - val_loss: 2.0808e-05
Epoch 322/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 9.7476e-06 - val_loss: 1.4807e-05
Epoch 323/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 1.3967e-05 - val_loss: 2.0725e-06
Epoch 324/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 2.6539e-06 - val_loss: 2.3632e-06
Epoch 325/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 1.4266e-05 - val_loss: 3.0255e-06
Epoch 326/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 5.6503e-06 - val_loss: 2.3852e-06
Epoch 327/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 2.8080e-05 - val_loss: 3.0900e-06
Epoch 328/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 2.4382e-06 - val_loss: 2.9060e-06
Epoch 329/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 9.3559e-06 - val_loss: 5.1858e-06
Epoch 330/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 9.0298e-06 - val_loss: 2.3302e-06
Epoch 331/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 2.3178e-06 - val_loss: 2.2415e-06
Epoch 332/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 1.8374e-05 - val_loss: 5.9787e-06
Epoch 333/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 4.1784e-06 - val_loss: 1.7912e-06
Epoch 334/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 6.7554e-06 - val_loss: 2.2403e-06
Epoch 335/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 1.0839e-05 - val_loss: 1.0753e-04
Epoch 336/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 1.9268e-05 - val_loss: 2.3432e-06
Epoch 337/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 2.7413e-06 - val_loss: 2.7888e-06
Epoch 338/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 2.1672e-05 - val_loss: 5.6468e-06
Epoch 339/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 2.1170e-06 - val_loss: 1.8866e-06
Epoch 340/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 2.8403e-06 - val_loss: 9.0113e-06
Epoch 341/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 1.8078e-05 - val_loss: 2.2221e-06
Epoch 342/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 3.0600e-06 - val_loss: 2.1397e-05
Epoch 343/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 1.5691e-05 - val_loss: 1.0458e-05
Epoch 344/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 5.1454e-06 - val_loss: 1.8779e-06
Epoch 345/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 2.1160e-05 - val_loss: 5.8678e-06
Epoch 346/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 2.0822e-06 - val_loss: 2.4306e-06
Epoch 347/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 6.6364e-06 - val_loss: 3.7464e-06
Epoch 348/1000
3888/3888 [==============================] - 1s 178us/sample - loss: 9.7320e-06 - val_loss: 3.6804e-06
Epoch 349/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 4.5874e-06 - val_loss: 2.0960e-06
Epoch 350/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 2.2424e-05 - val_loss: 4.8930e-06
Epoch 351/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 6.4397e-06 - val_loss: 1.9092e-06
Epoch 352/1000
3888/3888 [==============================] - 1s 180us/sample - loss: 2.5988e-06 - val_loss: 3.8970e-06
Epoch 353/1000
3888/3888 [==============================] - 1s 181us/sample - loss: 1.3001e-05 - val_loss: 1.1140e-05
Epoch 354/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 9.9668e-06 - val_loss: 1.7087e-06
Epoch 355/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 3.2530e-06 - val_loss: 2.1067e-06
Epoch 356/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 1.3583e-05 - val_loss: 1.5536e-05
Epoch 357/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 3.3133e-06 - val_loss: 1.9105e-06
Epoch 358/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 1.2270e-05 - val_loss: 8.5298e-06
Epoch 359/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 8.4059e-06 - val_loss: 6.9142e-06
Epoch 360/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 5.0029e-06 - val_loss: 2.9535e-06
Epoch 361/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 7.3502e-06 - val_loss: 2.2446e-04
Epoch 362/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 7.7992e-06 - val_loss: 1.0232e-05
Epoch 363/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 2.2512e-05 - val_loss: 2.1387e-06
Epoch 364/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 2.5206e-05 - val_loss: 2.6470e-06
Epoch 365/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 1.8718e-06 - val_loss: 1.7047e-06
Epoch 366/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 2.2842e-06 - val_loss: 2.4473e-06
Epoch 367/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 7.6799e-06 - val_loss: 4.1565e-05
Epoch 368/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 9.8564e-06 - val_loss: 9.8573e-06
Epoch 369/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 3.9651e-06 - val_loss: 7.1640e-06
Epoch 370/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 2.0943e-05 - val_loss: 2.0081e-05
Epoch 371/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 3.8441e-06 - val_loss: 1.5971e-06
Epoch 372/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 4.8798e-06 - val_loss: 2.0005e-06
Epoch 373/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 6.5734e-06 - val_loss: 1.6462e-06
Epoch 374/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 4.8593e-06 - val_loss: 1.2738e-05
Epoch 375/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 2.4957e-05 - val_loss: 1.8044e-06
Epoch 376/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 3.5031e-06 - val_loss: 3.2377e-06
Epoch 377/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 2.9384e-06 - val_loss: 1.7004e-06
Epoch 378/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 1.4203e-05 - val_loss: 1.4514e-05
Epoch 379/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 4.1299e-06 - val_loss: 4.3339e-06
Epoch 380/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 3.2026e-06 - val_loss: 2.5315e-06
Epoch 381/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 5.3867e-05 - val_loss: 3.9240e-06
Epoch 382/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 2.0593e-06 - val_loss: 1.8143e-06
Epoch 383/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 2.7075e-06 - val_loss: 1.7494e-06
Epoch 384/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 1.6606e-06 - val_loss: 1.6647e-06
Epoch 385/1000
3888/3888 [==============================] - 1s 180us/sample - loss: 6.9009e-06 - val_loss: 2.5021e-06
Epoch 386/1000
3888/3888 [==============================] - 1s 178us/sample - loss: 4.2177e-06 - val_loss: 2.3959e-06
Epoch 387/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 7.0853e-06 - val_loss: 7.7081e-06
Epoch 388/1000
3888/3888 [==============================] - 1s 180us/sample - loss: 1.8919e-05 - val_loss: 2.4233e-06
Epoch 389/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 2.9971e-06 - val_loss: 1.9259e-06
Epoch 390/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 1.9731e-06 - val_loss: 1.5496e-06
Epoch 391/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 3.7260e-06 - val_loss: 1.4467e-06
Epoch 392/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 2.0513e-05 - val_loss: 3.8704e-06
Epoch 393/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 4.3180e-06 - val_loss: 1.5908e-06
Epoch 394/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 2.2209e-06 - val_loss: 1.6939e-06
Epoch 395/1000
3888/3888 [==============================] - 1s 178us/sample - loss: 1.4494e-05 - val_loss: 2.9699e-06
Epoch 396/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 3.0743e-05 - val_loss: 5.9139e-05
Epoch 397/1000
3888/3888 [==============================] - 1s 180us/sample - loss: 3.6338e-05 - val_loss: 2.1397e-06
Epoch 398/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 1.7930e-06 - val_loss: 1.8215e-06
Epoch 399/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 1.7690e-06 - val_loss: 1.8423e-06
Epoch 400/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 1.7020e-06 - val_loss: 1.5541e-06
Epoch 401/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 2.6110e-06 - val_loss: 2.3584e-06
Epoch 402/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 1.8290e-05 - val_loss: 1.5521e-06
Epoch 403/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 1.5587e-06 - val_loss: 2.4611e-06
Epoch 404/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 4.1388e-06 - val_loss: 2.5938e-06
Epoch 405/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 9.9370e-06 - val_loss: 1.4917e-06
Epoch 406/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 4.0052e-06 - val_loss: 2.6830e-06
Epoch 407/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 5.3743e-06 - val_loss: 2.2690e-05
Epoch 408/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 3.5592e-05 - val_loss: 1.5562e-06
Epoch 409/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 1.5428e-06 - val_loss: 2.7522e-06
Epoch 410/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 2.7896e-06 - val_loss: 3.0423e-06
Epoch 411/1000
3888/3888 [==============================] - 1s 180us/sample - loss: 2.1510e-06 - val_loss: 1.5495e-05
Epoch 412/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 6.0791e-06 - val_loss: 1.4799e-06
Epoch 413/1000
3888/3888 [==============================] - 1s 179us/sample - loss: 1.0433e-05 - val_loss: 1.3086e-06
Epoch 414/1000
3888/3888 [==============================] - 1s 190us/sample - loss: 1.7596e-06 - val_loss: 4.1308e-06
Epoch 415/1000
3888/3888 [==============================] - 1s 179us/sample - loss: 1.5397e-05 - val_loss: 4.2626e-05
Epoch 416/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 3.6931e-06 - val_loss: 1.5022e-06
Epoch 417/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 3.5351e-05 - val_loss: 3.3211e-06
Epoch 418/1000
3888/3888 [==============================] - 1s 185us/sample - loss: 1.4503e-06 - val_loss: 1.1680e-06
Epoch 419/1000
3888/3888 [==============================] - 1s 178us/sample - loss: 1.6460e-06 - val_loss: 1.2937e-06
Epoch 420/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 1.6762e-06 - val_loss: 1.3255e-06
Epoch 421/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 1.1399e-05 - val_loss: 1.5501e-06
Epoch 422/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 3.0778e-06 - val_loss: 1.1850e-06
Epoch 423/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 1.0573e-05 - val_loss: 1.7515e-05
Epoch 424/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 4.0132e-06 - val_loss: 1.3778e-06
Epoch 425/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 4.7217e-06 - val_loss: 1.0759e-05
Epoch 426/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 5.7324e-06 - val_loss: 7.7046e-06
Epoch 427/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 1.5185e-05 - val_loss: 1.9901e-06
Epoch 428/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 1.0865e-05 - val_loss: 1.3619e-05
Epoch 429/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 2.2381e-06 - val_loss: 3.9273e-06
Epoch 430/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 9.2677e-06 - val_loss: 1.2386e-06
Epoch 431/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 1.0268e-05 - val_loss: 1.0382e-05
Epoch 432/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 9.2590e-06 - val_loss: 1.7713e-06
Epoch 433/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 1.6035e-06 - val_loss: 2.0373e-06
Epoch 434/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 3.3752e-06 - val_loss: 2.4552e-06
Epoch 435/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 1.7909e-05 - val_loss: 7.3176e-05
Epoch 436/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 6.5958e-06 - val_loss: 1.9673e-06
Epoch 437/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 6.8889e-06 - val_loss: 1.5430e-06
Epoch 438/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 5.2388e-06 - val_loss: 9.4830e-06
Epoch 439/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 2.4697e-06 - val_loss: 4.9742e-06
Epoch 440/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 2.2152e-05 - val_loss: 1.7234e-06
Epoch 441/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 3.3063e-06 - val_loss: 2.4266e-06
Epoch 442/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 2.8344e-06 - val_loss: 1.4443e-06
Epoch 443/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 1.8414e-05 - val_loss: 1.9922e-06
Epoch 444/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 8.0730e-06 - val_loss: 1.4204e-06
Epoch 445/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 1.1498e-05 - val_loss: 4.2393e-05
Epoch 446/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 3.0319e-06 - val_loss: 1.8748e-06
Epoch 447/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 2.1341e-06 - val_loss: 4.3277e-06
Epoch 448/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 2.0909e-05 - val_loss: 2.3485e-06
Epoch 449/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 1.2065e-06 - val_loss: 1.1550e-06
Epoch 450/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 1.5939e-06 - val_loss: 6.7722e-06
Epoch 451/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 1.0346e-05 - val_loss: 1.2055e-06
Epoch 452/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 1.0227e-05 - val_loss: 1.3773e-05
Epoch 453/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 3.0813e-06 - val_loss: 1.2987e-06
Epoch 454/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 3.5758e-06 - val_loss: 2.7455e-04
Epoch 455/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 2.5428e-05 - val_loss: 1.4038e-06
Epoch 456/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 2.3902e-06 - val_loss: 1.8620e-06
Epoch 457/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 9.3376e-06 - val_loss: 1.1622e-05
Epoch 458/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 1.0708e-05 - val_loss: 1.5163e-06
Epoch 459/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 3.6082e-06 - val_loss: 1.6680e-06
Epoch 460/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 1.5369e-06 - val_loss: 1.2157e-06
Epoch 461/1000
3888/3888 [==============================] - 1s 181us/sample - loss: 5.6462e-06 - val_loss: 1.4192e-06
Epoch 462/1000
3888/3888 [==============================] - 1s 179us/sample - loss: 1.1915e-05 - val_loss: 1.0203e-06
Epoch 463/1000
3888/3888 [==============================] - 1s 178us/sample - loss: 1.8084e-05 - val_loss: 1.0020e-06
Epoch 464/1000
3888/3888 [==============================] - 1s 178us/sample - loss: 1.2712e-06 - val_loss: 3.6884e-06
Epoch 465/1000
3888/3888 [==============================] - 1s 183us/sample - loss: 7.1238e-06 - val_loss: 3.9790e-06
Epoch 466/1000
3888/3888 [==============================] - 1s 178us/sample - loss: 1.2595e-05 - val_loss: 5.2550e-06
Epoch 467/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 7.1389e-06 - val_loss: 9.7097e-06
Epoch 468/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 3.4407e-06 - val_loss: 9.6495e-07
Epoch 469/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 1.4700e-06 - val_loss: 1.8017e-06
Epoch 470/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 1.2227e-05 - val_loss: 1.1400e-06
Epoch 471/1000
3888/3888 [==============================] - 1s 180us/sample - loss: 2.9678e-06 - val_loss: 9.4501e-06
Epoch 472/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 2.1334e-05 - val_loss: 1.1184e-06
Epoch 473/1000
3888/3888 [==============================] - 1s 179us/sample - loss: 2.5644e-06 - val_loss: 1.3535e-06
Epoch 474/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 2.0811e-05 - val_loss: 2.7761e-06
Epoch 475/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 1.0822e-06 - val_loss: 9.1740e-07
Epoch 476/1000
3888/3888 [==============================] - 1s 179us/sample - loss: 1.3874e-06 - val_loss: 1.0856e-06
Epoch 477/1000
3888/3888 [==============================] - 1s 178us/sample - loss: 6.1273e-06 - val_loss: 3.6102e-06
Epoch 478/1000
3888/3888 [==============================] - 1s 179us/sample - loss: 5.2369e-06 - val_loss: 1.5516e-06
Epoch 479/1000
3888/3888 [==============================] - 1s 180us/sample - loss: 1.2305e-05 - val_loss: 1.0515e-06
Epoch 480/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 1.4324e-06 - val_loss: 1.0629e-06
Epoch 481/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 2.7596e-05 - val_loss: 4.1984e-06
Epoch 482/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 1.2255e-06 - val_loss: 1.2940e-06
Epoch 483/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 1.1352e-06 - val_loss: 1.4913e-06
Epoch 484/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 1.5321e-06 - val_loss: 1.4226e-06
Epoch 485/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 2.1353e-05 - val_loss: 9.2207e-07
Epoch 486/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 1.1660e-06 - val_loss: 1.5253e-06
Epoch 487/1000
3888/3888 [==============================] - 1s 180us/sample - loss: 1.8730e-05 - val_loss: 7.4030e-06
Epoch 488/1000
3888/3888 [==============================] - 1s 180us/sample - loss: 1.7292e-06 - val_loss: 1.2631e-06
Epoch 489/1000
3888/3888 [==============================] - 1s 182us/sample - loss: 2.5786e-06 - val_loss: 1.0444e-06
Epoch 490/1000
3888/3888 [==============================] - 1s 178us/sample - loss: 2.8469e-06 - val_loss: 4.4975e-06
Epoch 491/1000
3888/3888 [==============================] - 1s 178us/sample - loss: 2.7076e-05 - val_loss: 1.6874e-06
Epoch 492/1000
3888/3888 [==============================] - 1s 183us/sample - loss: 1.0502e-06 - val_loss: 1.0931e-06
Epoch 493/1000
3888/3888 [==============================] - 1s 181us/sample - loss: 1.2618e-05 - val_loss: 1.8742e-06
Epoch 494/1000
3888/3888 [==============================] - 1s 182us/sample - loss: 2.8099e-06 - val_loss: 9.8976e-07
Epoch 495/1000
3888/3888 [==============================] - 1s 178us/sample - loss: 1.9711e-06 - val_loss: 9.0870e-07
Epoch 496/1000
3888/3888 [==============================] - 1s 179us/sample - loss: 3.3500e-06 - val_loss: 1.3082e-06
Epoch 497/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 1.0904e-05 - val_loss: 1.5501e-05
Epoch 498/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 1.2045e-05 - val_loss: 4.6749e-06
Epoch 499/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 5.6491e-06 - val_loss: 9.2892e-07
Epoch 500/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 1.0366e-06 - val_loss: 1.0301e-06
Epoch 501/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 1.2371e-05 - val_loss: 1.1822e-06
Epoch 502/1000
3888/3888 [==============================] - 1s 181us/sample - loss: 1.8258e-06 - val_loss: 1.0629e-06
Epoch 503/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 4.6098e-06 - val_loss: 3.5243e-05
Epoch 504/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 8.5696e-06 - val_loss: 8.1164e-06
Epoch 505/1000
3888/3888 [==============================] - 1s 178us/sample - loss: 7.9431e-06 - val_loss: 1.1133e-06
Epoch 506/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 1.8857e-06 - val_loss: 7.0038e-06
Epoch 507/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 2.1959e-05 - val_loss: 9.0636e-07
Epoch 508/1000
3888/3888 [==============================] - 1s 181us/sample - loss: 1.1916e-06 - val_loss: 8.9921e-07
Epoch 509/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 9.4945e-06 - val_loss: 2.0001e-04
Epoch 510/1000
3888/3888 [==============================] - 1s 180us/sample - loss: 6.8017e-06 - val_loss: 1.1831e-06
Epoch 511/1000
3888/3888 [==============================] - 1s 179us/sample - loss: 1.1870e-05 - val_loss: 1.0599e-06
Epoch 512/1000
3888/3888 [==============================] - 1s 178us/sample - loss: 1.7783e-06 - val_loss: 2.8202e-06
Epoch 513/1000
3888/3888 [==============================] - 1s 178us/sample - loss: 4.4915e-06 - val_loss: 8.2696e-06
Epoch 514/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 1.6106e-05 - val_loss: 2.2567e-06
Epoch 515/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 1.1247e-06 - val_loss: 1.2957e-06
Epoch 516/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 3.3345e-06 - val_loss: 7.6377e-06
Epoch 517/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 1.4168e-05 - val_loss: 1.5902e-06
Epoch 518/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 1.7137e-06 - val_loss: 1.1840e-06
Epoch 519/1000
3888/3888 [==============================] - 1s 178us/sample - loss: 1.6438e-05 - val_loss: 1.8122e-06
Epoch 520/1000
3888/3888 [==============================] - 1s 180us/sample - loss: 2.4871e-06 - val_loss: 1.4797e-06
Epoch 521/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 4.5490e-06 - val_loss: 8.0070e-07
Epoch 522/1000
3888/3888 [==============================] - 1s 179us/sample - loss: 3.4803e-05 - val_loss: 1.6581e-06
Epoch 523/1000
3888/3888 [==============================] - 1s 181us/sample - loss: 1.1841e-06 - val_loss: 9.4026e-07
Epoch 524/1000
3888/3888 [==============================] - 1s 179us/sample - loss: 1.0951e-06 - val_loss: 9.5357e-07
Epoch 525/1000
3888/3888 [==============================] - 1s 182us/sample - loss: 3.1694e-06 - val_loss: 1.3367e-06
Epoch 526/1000
3888/3888 [==============================] - 1s 183us/sample - loss: 5.8788e-06 - val_loss: 1.9314e-06
Epoch 527/1000
3888/3888 [==============================] - 1s 182us/sample - loss: 5.3872e-06 - val_loss: 3.8112e-06
Epoch 528/1000
3888/3888 [==============================] - 1s 180us/sample - loss: 5.6085e-06 - val_loss: 2.8063e-06
Epoch 529/1000
3888/3888 [==============================] - 1s 181us/sample - loss: 2.0449e-06 - val_loss: 2.1868e-06
Epoch 530/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 1.1434e-05 - val_loss: 5.5874e-05
Epoch 531/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 1.4473e-05 - val_loss: 6.3840e-04
Epoch 532/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 1.1531e-05 - val_loss: 9.8177e-07
Epoch 533/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 1.2701e-06 - val_loss: 1.1586e-06
Epoch 534/1000
3888/3888 [==============================] - 1s 181us/sample - loss: 3.9397e-06 - val_loss: 9.8483e-06
Epoch 535/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 4.9912e-06 - val_loss: 8.5707e-07
Epoch 536/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 2.5146e-06 - val_loss: 5.3427e-06
Epoch 537/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 1.0996e-05 - val_loss: 3.2856e-06
Epoch 538/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 8.8692e-06 - val_loss: 1.7211e-05
Epoch 539/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 4.3567e-06 - val_loss: 9.3089e-07
Epoch 540/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 6.4488e-06 - val_loss: 1.7783e-06
Epoch 541/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 2.3697e-06 - val_loss: 4.2840e-05
Epoch 542/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 1.6074e-05 - val_loss: 9.5374e-07
Epoch 543/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 1.2452e-06 - val_loss: 5.2246e-06
Epoch 544/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 1.5425e-05 - val_loss: 2.3539e-06
Epoch 545/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 1.4071e-06 - val_loss: 4.8955e-06
Epoch 546/1000
3888/3888 [==============================] - 1s 179us/sample - loss: 4.3799e-06 - val_loss: 1.3718e-05
Epoch 547/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 1.1709e-05 - val_loss: 6.7364e-06
Epoch 548/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 1.7424e-06 - val_loss: 9.7109e-07
Epoch 549/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 2.4036e-05 - val_loss: 6.8480e-06
Epoch 550/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 2.2266e-06 - val_loss: 1.0541e-06
Epoch 551/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 1.0686e-06 - val_loss: 2.0343e-06
Epoch 552/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 6.6201e-06 - val_loss: 1.8123e-06
Epoch 553/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 1.4260e-05 - val_loss: 8.2746e-06
Epoch 554/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 1.2413e-06 - val_loss: 1.7275e-06
Epoch 555/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 5.1350e-06 - val_loss: 2.8627e-06
Epoch 556/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 6.1295e-06 - val_loss: 1.0613e-06
Epoch 557/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 2.1084e-06 - val_loss: 1.9802e-06
Epoch 558/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 9.0013e-06 - val_loss: 3.9983e-05
Epoch 559/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 1.1063e-05 - val_loss: 2.9720e-06
Epoch 560/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 2.2798e-06 - val_loss: 1.1325e-06
Epoch 561/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 4.1539e-06 - val_loss: 6.9933e-07
Epoch 562/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 5.8772e-06 - val_loss: 1.5867e-06
Epoch 563/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 1.4767e-05 - val_loss: 1.0609e-06
Epoch 564/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 1.1623e-06 - val_loss: 7.6949e-07
Epoch 565/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 5.5857e-06 - val_loss: 4.4581e-06
Epoch 566/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 9.4753e-06 - val_loss: 7.1253e-07
Epoch 567/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 2.6149e-06 - val_loss: 1.3393e-06
Epoch 568/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 8.0726e-06 - val_loss: 8.3753e-06
Epoch 569/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 1.5192e-05 - val_loss: 5.1985e-06
Epoch 570/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 2.2698e-06 - val_loss: 1.0956e-06
Epoch 571/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 9.7896e-07 - val_loss: 1.3943e-06
Epoch 572/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 1.2841e-05 - val_loss: 1.0330e-06
Epoch 573/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 2.6088e-06 - val_loss: 1.7922e-05
Epoch 574/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 2.1366e-05 - val_loss: 1.1295e-06
Epoch 575/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 9.3262e-07 - val_loss: 8.2604e-07
Epoch 576/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 1.1485e-06 - val_loss: 1.9711e-06
Epoch 577/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 5.3565e-06 - val_loss: 1.1141e-06
Epoch 578/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 3.1675e-06 - val_loss: 1.1055e-06
Epoch 579/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 1.0166e-05 - val_loss: 1.0169e-06
Epoch 580/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 5.4294e-06 - val_loss: 1.0586e-06
Epoch 581/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 3.3334e-06 - val_loss: 2.0306e-06
Epoch 582/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 9.7737e-06 - val_loss: 9.6329e-07
Epoch 583/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 3.3132e-06 - val_loss: 2.9515e-06
Epoch 584/1000
3888/3888 [==============================] - 1s 179us/sample - loss: 8.3841e-06 - val_loss: 8.0121e-07
Epoch 585/1000
3888/3888 [==============================] - 1s 179us/sample - loss: 1.3057e-06 - val_loss: 1.4063e-06
Epoch 586/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 1.3206e-05 - val_loss: 8.1323e-07
Epoch 587/1000
3888/3888 [==============================] - 1s 179us/sample - loss: 1.0071e-06 - val_loss: 1.7559e-05
Epoch 588/1000
3888/3888 [==============================] - 1s 178us/sample - loss: 6.4735e-06 - val_loss: 1.1089e-05
Epoch 589/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 1.0442e-05 - val_loss: 8.7763e-07
Epoch 590/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 3.7262e-06 - val_loss: 7.2418e-06
Epoch 591/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 9.4999e-06 - val_loss: 9.1392e-07
Epoch 592/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 1.9401e-06 - val_loss: 1.4601e-05
Epoch 593/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 6.0707e-06 - val_loss: 2.8764e-05
Epoch 594/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 1.2990e-05 - val_loss: 6.7774e-06
Epoch 595/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 1.4543e-05 - val_loss: 6.4575e-05
Epoch 596/1000
3888/3888 [==============================] - 1s 178us/sample - loss: 2.1851e-06 - val_loss: 1.0611e-06
Epoch 597/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 4.2005e-06 - val_loss: 1.0698e-06
Epoch 598/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 3.7407e-06 - val_loss: 4.2647e-06
Epoch 599/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 2.2542e-06 - val_loss: 1.9349e-06
Epoch 600/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 1.3838e-05 - val_loss: 1.1409e-06
Epoch 601/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 1.5660e-06 - val_loss: 1.2980e-06
Epoch 602/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 9.9212e-06 - val_loss: 1.5549e-06
Epoch 603/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 1.0960e-06 - val_loss: 9.9821e-07
Epoch 604/1000
3888/3888 [==============================] - 1s 179us/sample - loss: 2.5465e-05 - val_loss: 1.3675e-06
Epoch 605/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 8.3191e-07 - val_loss: 9.2478e-07
Epoch 606/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 1.4061e-06 - val_loss: 8.4134e-07
Epoch 607/1000
3888/3888 [==============================] - 1s 180us/sample - loss: 1.9697e-06 - val_loss: 1.7388e-06
Epoch 608/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 1.7452e-05 - val_loss: 3.1190e-06
Epoch 609/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 1.4322e-06 - val_loss: 9.2444e-07
Epoch 610/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 2.5410e-06 - val_loss: 1.4310e-06
Epoch 611/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 6.1103e-06 - val_loss: 1.5369e-06
Epoch 612/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 3.5616e-06 - val_loss: 2.2030e-06
Epoch 613/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 1.1420e-05 - val_loss: 4.3294e-04
Epoch 614/1000
3888/3888 [==============================] - 1s 181us/sample - loss: 9.9325e-06 - val_loss: 8.3826e-07
Epoch 615/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 1.4105e-06 - val_loss: 9.8381e-07
Epoch 616/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 9.9980e-06 - val_loss: 3.0689e-05
Epoch 617/1000
3888/3888 [==============================] - 1s 179us/sample - loss: 5.2282e-06 - val_loss: 8.7572e-07
Epoch 618/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 1.9242e-06 - val_loss: 6.9489e-07
Epoch 619/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 4.9974e-06 - val_loss: 1.3678e-06
Epoch 620/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 9.2496e-06 - val_loss: 7.3139e-07
Epoch 621/1000
3888/3888 [==============================] - 1s 184us/sample - loss: 8.3775e-07 - val_loss: 1.2029e-06
Epoch 622/1000
3888/3888 [==============================] - 1s 182us/sample - loss: 8.3227e-06 - val_loss: 2.7065e-05
Epoch 623/1000
3888/3888 [==============================] - 1s 182us/sample - loss: 1.6273e-05 - val_loss: 6.7766e-07
Epoch 624/1000
3888/3888 [==============================] - 1s 182us/sample - loss: 8.2391e-07 - val_loss: 7.2137e-07
Epoch 625/1000
3888/3888 [==============================] - 1s 181us/sample - loss: 9.6558e-07 - val_loss: 7.7050e-07
Epoch 626/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 1.3097e-05 - val_loss: 1.5587e-06
Epoch 627/1000
3888/3888 [==============================] - 1s 178us/sample - loss: 1.4848e-06 - val_loss: 8.8139e-07
Epoch 628/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 1.6687e-05 - val_loss: 1.7469e-06
Epoch 629/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 8.4280e-07 - val_loss: 7.7586e-07
Epoch 630/1000
3888/3888 [==============================] - 1s 178us/sample - loss: 2.1256e-06 - val_loss: 2.8311e-06
Epoch 631/1000
3888/3888 [==============================] - 1s 184us/sample - loss: 1.8128e-05 - val_loss: 8.6366e-07
Epoch 632/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 1.0335e-06 - val_loss: 7.3205e-07
Epoch 633/1000
3888/3888 [==============================] - 1s 178us/sample - loss: 4.5946e-06 - val_loss: 2.7754e-06
Epoch 634/1000
3888/3888 [==============================] - 1s 179us/sample - loss: 3.6458e-06 - val_loss: 7.5278e-07
Epoch 635/1000
3888/3888 [==============================] - 1s 179us/sample - loss: 5.1918e-06 - val_loss: 7.2803e-07
Epoch 636/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 5.2323e-06 - val_loss: 6.4537e-07
Epoch 637/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 3.7709e-06 - val_loss: 7.2236e-07
Epoch 638/1000
3888/3888 [==============================] - 1s 182us/sample - loss: 1.5383e-05 - val_loss: 1.0185e-06
Epoch 639/1000
3888/3888 [==============================] - 1s 183us/sample - loss: 7.7825e-06 - val_loss: 7.6979e-05
Epoch 640/1000
3888/3888 [==============================] - 1s 179us/sample - loss: 6.3464e-06 - val_loss: 3.1776e-05
Epoch 641/1000
3888/3888 [==============================] - 1s 183us/sample - loss: 8.1383e-06 - val_loss: 7.3504e-07
Epoch 642/1000
3888/3888 [==============================] - 1s 183us/sample - loss: 7.0134e-07 - val_loss: 1.2657e-06
Epoch 643/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 1.8160e-06 - val_loss: 6.4372e-07
Epoch 644/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 1.8117e-05 - val_loss: 8.9588e-07
Epoch 645/1000
3888/3888 [==============================] - 1s 178us/sample - loss: 8.2953e-07 - val_loss: 7.4985e-07
Epoch 646/1000
3888/3888 [==============================] - 1s 179us/sample - loss: 1.2635e-06 - val_loss: 1.2836e-06
Epoch 647/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 4.0888e-05 - val_loss: 1.7435e-05
Epoch 648/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 4.8301e-06 - val_loss: 1.0895e-06
Epoch 649/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 9.9777e-07 - val_loss: 9.9591e-07
Epoch 650/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 8.9786e-07 - val_loss: 8.0778e-07
Epoch 651/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 1.3470e-06 - val_loss: 1.0469e-05
Epoch 652/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 3.0853e-06 - val_loss: 9.0029e-07
Epoch 653/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 5.6292e-06 - val_loss: 7.4468e-07
Epoch 654/1000
3888/3888 [==============================] - 1s 178us/sample - loss: 1.5883e-05 - val_loss: 7.1694e-07
Epoch 655/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 9.6981e-07 - val_loss: 2.3582e-06
Epoch 656/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 9.5429e-07 - val_loss: 7.0387e-07
Epoch 657/1000
3888/3888 [==============================] - 1s 181us/sample - loss: 8.6699e-06 - val_loss: 7.4278e-06
Epoch 658/1000
3888/3888 [==============================] - 1s 180us/sample - loss: 3.7977e-06 - val_loss: 1.0903e-06
Epoch 659/1000
3888/3888 [==============================] - 1s 188us/sample - loss: 1.5148e-06 - val_loss: 3.1882e-06
Epoch 660/1000
3888/3888 [==============================] - 1s 183us/sample - loss: 1.3912e-05 - val_loss: 1.0949e-06
Epoch 661/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 1.5234e-06 - val_loss: 3.9370e-06
Epoch 662/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 1.0094e-05 - val_loss: 1.4405e-05
Epoch 663/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 2.0956e-06 - val_loss: 1.2220e-06
Epoch 664/1000
3888/3888 [==============================] - 1s 181us/sample - loss: 1.5128e-06 - val_loss: 1.9773e-05
Epoch 665/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 6.9733e-06 - val_loss: 8.7898e-07
Epoch 666/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 8.7990e-06 - val_loss: 2.6586e-05
Epoch 667/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 8.1423e-06 - val_loss: 8.4490e-07
Epoch 668/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 1.1550e-05 - val_loss: 3.1634e-04
Epoch 669/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 7.7539e-06 - val_loss: 6.4227e-07
Epoch 670/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 7.8929e-07 - val_loss: 7.7161e-07
Epoch 671/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 1.8981e-06 - val_loss: 3.9827e-05
Epoch 672/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 6.9435e-06 - val_loss: 7.5519e-07
Epoch 673/1000
3888/3888 [==============================] - 1s 180us/sample - loss: 4.9098e-06 - val_loss: 7.8272e-07
Epoch 674/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 1.5963e-05 - val_loss: 1.1199e-05
Epoch 675/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 1.3209e-06 - val_loss: 7.1964e-07
Epoch 676/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 7.8819e-07 - val_loss: 7.7390e-07
Epoch 677/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 4.8237e-06 - val_loss: 9.5430e-07
Epoch 678/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 5.6823e-06 - val_loss: 1.2342e-06
Epoch 679/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 1.6249e-05 - val_loss: 7.6970e-07
Epoch 680/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 1.4951e-06 - val_loss: 1.7948e-06
Epoch 681/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 1.8288e-06 - val_loss: 1.9353e-06
Epoch 682/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 1.2006e-05 - val_loss: 6.8243e-07
Epoch 683/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 9.5268e-06 - val_loss: 2.6148e-05
Epoch 684/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 6.6634e-06 - val_loss: 6.8921e-07
Epoch 685/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 1.3126e-06 - val_loss: 1.3975e-06
Epoch 686/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 1.0464e-06 - val_loss: 7.2380e-07
Epoch 687/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 2.2062e-05 - val_loss: 9.0180e-07
Epoch 688/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 7.5753e-07 - val_loss: 7.5016e-07
Epoch 689/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 1.1400e-06 - val_loss: 8.2846e-07
Epoch 690/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 2.2835e-06 - val_loss: 1.3102e-05
Epoch 691/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 3.4549e-06 - val_loss: 9.5385e-07
Epoch 692/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 5.2391e-06 - val_loss: 4.5766e-06
Epoch 693/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 8.6873e-06 - val_loss: 8.5413e-07
Epoch 694/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 8.3717e-06 - val_loss: 2.9855e-06
Epoch 695/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 2.1306e-06 - val_loss: 7.3996e-06
Epoch 696/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 6.3075e-06 - val_loss: 5.7341e-05
Epoch 697/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 6.8246e-06 - val_loss: 5.8163e-07
Epoch 698/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 6.7242e-06 - val_loss: 7.4032e-07
Epoch 699/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 1.4336e-06 - val_loss: 1.0061e-06
Epoch 700/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 1.2117e-05 - val_loss: 2.3619e-06
Epoch 701/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 2.4556e-06 - val_loss: 7.4717e-07
Epoch 702/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 2.1042e-06 - val_loss: 1.8059e-05
Epoch 703/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 2.1900e-05 - val_loss: 1.5014e-06
Epoch 704/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 7.6848e-07 - val_loss: 7.6811e-07
Epoch 705/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 2.3493e-06 - val_loss: 9.0395e-07
Epoch 706/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 3.4063e-06 - val_loss: 9.8102e-07
Epoch 707/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 5.8934e-06 - val_loss: 4.6523e-06
Epoch 708/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 7.1316e-06 - val_loss: 7.0721e-07
Epoch 709/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 6.8110e-06 - val_loss: 1.3486e-04
Epoch 710/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 4.8879e-06 - val_loss: 7.0668e-07
Epoch 711/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 1.0105e-05 - val_loss: 5.9505e-07
Epoch 712/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 8.6121e-07 - val_loss: 1.1204e-06
Epoch 713/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 2.8833e-06 - val_loss: 1.2931e-06
Epoch 714/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 4.8541e-06 - val_loss: 1.9471e-06
Epoch 715/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 1.0477e-05 - val_loss: 1.1235e-06
Epoch 716/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 1.0810e-06 - val_loss: 1.5658e-05
Epoch 717/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 8.0205e-06 - val_loss: 3.8845e-06
Epoch 718/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 1.0286e-05 - val_loss: 3.3220e-05
Epoch 719/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 2.3179e-06 - val_loss: 7.3284e-07
Epoch 720/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 4.1861e-06 - val_loss: 5.1867e-05
Epoch 721/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 5.7525e-05 - val_loss: 2.2818e-06
Epoch 722/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 1.8037e-06 - val_loss: 1.5087e-06
Epoch 723/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 1.3043e-06 - val_loss: 1.1848e-06
Epoch 724/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 1.0499e-06 - val_loss: 1.0393e-06
Epoch 725/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 9.0199e-07 - val_loss: 9.5415e-07
Epoch 726/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 8.1322e-07 - val_loss: 7.6777e-07
Epoch 727/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 1.8133e-06 - val_loss: 8.0737e-07
Epoch 728/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 5.4747e-06 - val_loss: 6.0281e-07
Epoch 729/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 8.4922e-06 - val_loss: 7.0930e-07
Epoch 730/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 1.7676e-06 - val_loss: 1.4913e-05
Epoch 731/1000
3888/3888 [==============================] - 1s 182us/sample - loss: 1.4925e-05 - val_loss: 7.0219e-07
Epoch 732/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 6.4842e-07 - val_loss: 6.2637e-07
Epoch 733/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 1.7327e-06 - val_loss: 2.0911e-06
Epoch 734/1000
3888/3888 [==============================] - 1s 178us/sample - loss: 8.2876e-07 - val_loss: 1.6520e-06
Epoch 735/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 6.7580e-06 - val_loss: 6.3923e-07
Epoch 736/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 6.2587e-06 - val_loss: 5.6521e-07
Epoch 737/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 6.0941e-06 - val_loss: 1.8331e-06
Epoch 738/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 3.6363e-06 - val_loss: 2.8624e-06
Epoch 739/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 4.9162e-06 - val_loss: 9.1887e-07
Epoch 740/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 4.8136e-06 - val_loss: 2.8889e-06
Epoch 741/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 1.0219e-05 - val_loss: 1.5179e-06
Epoch 742/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 1.6289e-06 - val_loss: 9.6012e-07
Epoch 743/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 3.5600e-06 - val_loss: 1.2168e-05
Epoch 744/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 1.7416e-05 - val_loss: 7.4877e-07
Epoch 745/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 1.9651e-06 - val_loss: 1.2680e-06
Epoch 746/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 1.7443e-06 - val_loss: 9.3088e-07
Epoch 747/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 2.9264e-06 - val_loss: 9.1670e-07
Epoch 748/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 1.3574e-05 - val_loss: 1.4566e-06
Epoch 749/1000
3888/3888 [==============================] - 1s 181us/sample - loss: 4.5306e-06 - val_loss: 4.7483e-05
Epoch 750/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 2.8890e-06 - val_loss: 9.5971e-07
Epoch 751/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 1.9582e-06 - val_loss: 2.1464e-06
Epoch 752/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 5.9634e-06 - val_loss: 8.4306e-06
Epoch 753/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 6.6928e-06 - val_loss: 1.6245e-06
Epoch 754/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 2.1763e-06 - val_loss: 3.2276e-06
Epoch 755/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 9.2350e-06 - val_loss: 2.9095e-05
Epoch 756/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 1.2655e-05 - val_loss: 6.7576e-07
Epoch 757/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 6.3465e-07 - val_loss: 7.1135e-07
Epoch 758/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 1.2059e-06 - val_loss: 1.5243e-05
Epoch 759/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 6.8060e-06 - val_loss: 6.1481e-07
Epoch 760/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 6.7133e-06 - val_loss: 1.6449e-06
Epoch 761/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 7.6168e-06 - val_loss: 6.3043e-07
Epoch 762/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 3.8273e-06 - val_loss: 6.3771e-07
Epoch 763/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 6.4535e-06 - val_loss: 3.9212e-06
Epoch 764/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 1.3539e-06 - val_loss: 1.6992e-06
Epoch 765/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 1.8220e-05 - val_loss: 1.5792e-05
Epoch 766/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 4.7985e-06 - val_loss: 6.0404e-07
Epoch 767/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 6.0610e-07 - val_loss: 5.8896e-07
Epoch 768/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 2.3525e-06 - val_loss: 7.7699e-07
Epoch 769/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 1.6050e-05 - val_loss: 8.5060e-07
Epoch 770/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 7.1942e-07 - val_loss: 2.7657e-06
Epoch 771/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 1.3401e-06 - val_loss: 5.6950e-07
Epoch 772/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 1.5105e-05 - val_loss: 1.8599e-06
Epoch 773/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 8.4921e-07 - val_loss: 1.0381e-06
Epoch 774/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 1.0297e-06 - val_loss: 1.8987e-06
Epoch 775/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 8.3791e-06 - val_loss: 2.9625e-06
Epoch 776/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 1.8342e-06 - val_loss: 6.7098e-07
Epoch 777/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 1.9755e-06 - val_loss: 7.7628e-06
Epoch 778/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 2.1838e-05 - val_loss: 9.9436e-07
Epoch 779/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 9.3956e-07 - val_loss: 1.1311e-06
Epoch 780/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 1.2513e-06 - val_loss: 2.9211e-06
Epoch 781/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 4.6201e-06 - val_loss: 5.8133e-05
Epoch 782/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 5.7523e-06 - val_loss: 6.5833e-07
Epoch 783/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 1.3883e-06 - val_loss: 5.7518e-07
Epoch 784/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 1.3109e-05 - val_loss: 6.4882e-07
Epoch 785/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 9.1963e-07 - val_loss: 7.7708e-07
Epoch 786/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 3.6003e-06 - val_loss: 9.2990e-07
Epoch 787/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 3.4349e-05 - val_loss: 1.6265e-06
Epoch 788/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 1.0615e-06 - val_loss: 8.6446e-07
Epoch 789/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 8.2021e-07 - val_loss: 7.3159e-07
Epoch 790/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 7.5446e-07 - val_loss: 1.2184e-06
Epoch 791/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 1.8326e-06 - val_loss: 1.1649e-06
Epoch 792/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 6.5281e-06 - val_loss: 5.2580e-05
Epoch 793/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 2.8588e-06 - val_loss: 1.9291e-06
Epoch 794/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 9.9394e-06 - val_loss: 1.0689e-06
Epoch 795/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 6.7191e-07 - val_loss: 6.1267e-07
Epoch 796/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 3.0218e-06 - val_loss: 8.6557e-07
Epoch 797/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 1.2108e-05 - val_loss: 1.0201e-04
Epoch 798/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 1.2425e-05 - val_loss: 7.2808e-07
Epoch 799/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 7.0304e-07 - val_loss: 9.6760e-07
Epoch 800/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 7.1558e-07 - val_loss: 5.5140e-06
Epoch 801/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 2.0549e-06 - val_loss: 1.0227e-05
Epoch 802/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 1.0821e-05 - val_loss: 1.1289e-06
Epoch 803/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 4.2101e-06 - val_loss: 6.4077e-07
Epoch 804/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 6.6842e-07 - val_loss: 1.3499e-06
Epoch 805/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 5.2447e-06 - val_loss: 9.8721e-07
Epoch 806/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 7.1325e-06 - val_loss: 1.0566e-04
Epoch 807/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 1.3198e-05 - val_loss: 9.1788e-07
Epoch 808/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 1.0705e-06 - val_loss: 9.6235e-07
Epoch 809/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 6.5115e-06 - val_loss: 1.7538e-05
Epoch 810/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 1.8441e-05 - val_loss: 9.0477e-07
Epoch 811/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 8.3060e-07 - val_loss: 7.6046e-07
Epoch 812/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 8.3633e-07 - val_loss: 8.1277e-07
Epoch 813/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 1.1468e-05 - val_loss: 7.3523e-07
Epoch 814/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 7.0558e-07 - val_loss: 6.6015e-07
Epoch 815/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 4.1218e-06 - val_loss: 1.4832e-05
Epoch 816/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 1.3783e-06 - val_loss: 7.2879e-07
Epoch 817/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 4.5321e-06 - val_loss: 5.8072e-06
Epoch 818/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 4.2149e-06 - val_loss: 2.2895e-05
Epoch 819/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 3.5535e-06 - val_loss: 4.5771e-06
Epoch 820/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 5.0600e-05 - val_loss: 1.6808e-06
Epoch 821/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 1.2665e-06 - val_loss: 1.2115e-06
Epoch 822/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 1.0530e-06 - val_loss: 9.3358e-07
Epoch 823/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 8.9179e-07 - val_loss: 8.2348e-07
Epoch 824/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 7.9828e-07 - val_loss: 7.9668e-07
Epoch 825/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 6.7851e-06 - val_loss: 7.2348e-07
Epoch 826/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 1.5011e-06 - val_loss: 7.4602e-07
Epoch 827/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 1.4603e-06 - val_loss: 1.5703e-06
Epoch 828/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 1.0309e-05 - val_loss: 1.0815e-05
Epoch 829/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 1.2254e-06 - val_loss: 3.3511e-06
Epoch 830/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 3.3901e-06 - val_loss: 5.2407e-05
Epoch 831/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 2.9628e-06 - val_loss: 2.0404e-06
Epoch 832/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 5.4720e-06 - val_loss: 9.3301e-07
Epoch 833/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 1.7215e-06 - val_loss: 3.0003e-06
Epoch 834/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 2.0808e-05 - val_loss: 7.2190e-07
Epoch 835/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 8.8221e-07 - val_loss: 7.6455e-07
Epoch 836/1000
3840/3888 [============================>.] - ETA: 0s - loss: 1.5360e-06Restoring model weights from the end of the best epoch.
3888/3888 [==============================] - 1s 177us/sample - loss: 1.6198e-06 - val_loss: 1.0451e-05
Epoch 00836: early stopping
In [39]:
print(history.history.keys())
print('best value: ', autoencoder.evaluate(X_train_1D_norm, X_train_1D_norm, verbose=0))


pd.DataFrame(history.history).plot(figsize=(8, 5), logy=True)
plt.grid()
dict_keys(['loss', 'val_loss'])
best value:  5.652084395026656e-07
In [40]:
X_reconstructions = autoencoder.predict(X_train_1D_norm)
X_reconstructions = stdscaler.inverse_transform(X_reconstructions)
In [41]:
calculateerror(X_train_1D.reshape(len(times),len(groups),nl,nc), 
               X_reconstructions.reshape(len(times),len(groups),nl,nc), 
               groups,
               print_step=0)
max_abs_error:  5.275390625
mean_abs_error:  0.015190057506593278
/home/viluiz/anaconda3/envs/py3ml/lib/python3.7/site-packages/ipykernel_launcher.py:3: RuntimeWarning: divide by zero encountered in true_divide
  This is separate from the ipykernel package so we can avoid doing imports until
/home/viluiz/anaconda3/envs/py3ml/lib/python3.7/site-packages/ipykernel_launcher.py:3: RuntimeWarning: invalid value encountered in true_divide
  This is separate from the ipykernel package so we can avoid doing imports until
In [42]:
fig, ax = plt.subplots(2,4, figsize=[20,10])
for i, group in enumerate(groups):
    im = ax.flatten()[i].imshow(X_reconstructions.reshape(len(times),len(groups),nl,nc)[100,i,:,:])
    fig.colorbar(im, ax=ax.flatten()[i])
    ax.flatten()[i].set_title(group)
In [43]:
fig, ax = plt.subplots(2,4, figsize=[20,10])
for i, group in enumerate(groups):
    ax.flatten()[i].plot(times, X_train_1D[:,i*nl*nc+4])
    ax.flatten()[i].plot(times, X_reconstructions[:,i*nl*nc+4],'--')
    ax.flatten()[i].set_title(group)

Non-linear autoencoder

In [44]:
np.random.seed(42)
tf.random.set_seed(42)

# Need to have validation loss
early_stopping = keras.callbacks.EarlyStopping(monitor='val_loss',
                                               min_delta=0.0,
                                               patience=100,
                                               verbose=2,
                                               restore_best_weights=True)

encoder = keras.models.Sequential([keras.layers.Dense(100, input_shape=[800], activation="elu"),
                                   keras.layers.Dense(50, activation="elu"),
                                   keras.layers.Dense(15)])
decoder = keras.models.Sequential([keras.layers.Dense(50, input_shape=[15], activation="elu"),
                                   keras.layers.Dense(100, activation="elu"),
                                   keras.layers.Dense(800),
                                  ])
autoencoder = keras.models.Sequential([encoder, decoder])

autoencoder.compile(loss="mse", 
                    optimizer=keras.optimizers.Nadam(lr=0.0003, beta_1=0.9, beta_2=0.999)
                    )
encoder.summary()
decoder.summary()
Model: "sequential_6"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_6 (Dense)              (None, 100)               80100     
_________________________________________________________________
dense_7 (Dense)              (None, 50)                5050      
_________________________________________________________________
dense_8 (Dense)              (None, 15)                765       
=================================================================
Total params: 85,915
Trainable params: 85,915
Non-trainable params: 0
_________________________________________________________________
Model: "sequential_7"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_9 (Dense)              (None, 50)                800       
_________________________________________________________________
dense_10 (Dense)             (None, 100)               5100      
_________________________________________________________________
dense_11 (Dense)             (None, 800)               80800     
=================================================================
Total params: 86,700
Trainable params: 86,700
Non-trainable params: 0
_________________________________________________________________
In [45]:
history = autoencoder.fit(X_train_1D_norm, 
                          X_train_1D_norm, 
                          epochs=1000,
                          validation_data=(X_train_1D_norm, X_train_1D_norm),
                          callbacks=[early_stopping])
Train on 3888 samples, validate on 3888 samples
Epoch 1/1000
3888/3888 [==============================] - 2s 399us/sample - loss: 0.0738 - val_loss: 0.0213
Epoch 2/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 0.0133 - val_loss: 0.0077
Epoch 3/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 0.0052 - val_loss: 0.0039
Epoch 4/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 0.0028 - val_loss: 0.0019
Epoch 5/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 0.0016 - val_loss: 0.0014
Epoch 6/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 0.0011 - val_loss: 9.0248e-04
Epoch 7/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 8.9075e-04 - val_loss: 8.0369e-04
Epoch 8/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 6.9541e-04 - val_loss: 5.6254e-04
Epoch 9/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 6.2126e-04 - val_loss: 4.4743e-04
Epoch 10/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 4.3809e-04 - val_loss: 4.5001e-04
Epoch 11/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 4.3138e-04 - val_loss: 3.2076e-04
Epoch 12/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 4.2502e-04 - val_loss: 3.9755e-04
Epoch 13/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 3.5841e-04 - val_loss: 2.4384e-04
Epoch 14/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 2.9576e-04 - val_loss: 2.2604e-04
Epoch 15/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 2.6657e-04 - val_loss: 2.0372e-04
Epoch 16/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 3.3324e-04 - val_loss: 1.9059e-04
Epoch 17/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 2.2792e-04 - val_loss: 1.8347e-04
Epoch 18/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 1.7011e-04 - val_loss: 2.6279e-04
Epoch 19/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 2.4473e-04 - val_loss: 1.5079e-04
Epoch 20/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 2.1813e-04 - val_loss: 1.3292e-04
Epoch 21/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 1.4057e-04 - val_loss: 1.1185e-04
Epoch 22/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 1.6320e-04 - val_loss: 1.2385e-04
Epoch 23/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 1.6112e-04 - val_loss: 1.1233e-04
Epoch 24/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 2.2680e-04 - val_loss: 1.2117e-04
Epoch 25/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 1.0080e-04 - val_loss: 8.9527e-05
Epoch 26/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 1.8405e-04 - val_loss: 2.4367e-04
Epoch 27/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 1.0457e-04 - val_loss: 1.6173e-04
Epoch 28/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 1.2771e-04 - val_loss: 1.1469e-04
Epoch 29/1000
3888/3888 [==============================] - 1s 178us/sample - loss: 1.1713e-04 - val_loss: 3.4487e-04
Epoch 30/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 1.1976e-04 - val_loss: 3.4356e-04
Epoch 31/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 2.0136e-04 - val_loss: 6.7224e-05
Epoch 32/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 7.7168e-05 - val_loss: 8.2778e-05
Epoch 33/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 1.4334e-04 - val_loss: 6.7871e-05
Epoch 34/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 8.6404e-05 - val_loss: 6.4198e-05
Epoch 35/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 7.6001e-05 - val_loss: 1.2657e-04
Epoch 36/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 1.1915e-04 - val_loss: 5.4725e-05
Epoch 37/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 6.2745e-05 - val_loss: 7.7216e-05
Epoch 38/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 1.1707e-04 - val_loss: 6.1423e-05
Epoch 39/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 7.0213e-05 - val_loss: 5.9363e-05
Epoch 40/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 2.8709e-04 - val_loss: 5.8366e-05
Epoch 41/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 5.0724e-05 - val_loss: 4.5737e-05
Epoch 42/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 5.0222e-05 - val_loss: 8.8702e-05
Epoch 43/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 6.3829e-05 - val_loss: 8.8226e-05
Epoch 44/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 5.6035e-05 - val_loss: 6.1917e-05
Epoch 45/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 5.9398e-05 - val_loss: 1.4935e-04
Epoch 46/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 1.0370e-04 - val_loss: 3.9682e-05
Epoch 47/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 7.8800e-05 - val_loss: 4.5692e-05
Epoch 48/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 4.5110e-05 - val_loss: 3.9648e-05
Epoch 49/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 1.2317e-04 - val_loss: 3.4771e-05
Epoch 50/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 4.2581e-05 - val_loss: 5.9154e-05
Epoch 51/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 5.7513e-05 - val_loss: 3.4424e-05
Epoch 52/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 5.1373e-05 - val_loss: 3.7781e-05
Epoch 53/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 6.2329e-05 - val_loss: 3.3030e-05
Epoch 54/1000
3888/3888 [==============================] - 1s 162us/sample - loss: 9.8988e-05 - val_loss: 3.8013e-05
Epoch 55/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 3.4162e-05 - val_loss: 3.0226e-05
Epoch 56/1000
3888/3888 [==============================] - 1s 160us/sample - loss: 3.5709e-05 - val_loss: 4.9157e-05
Epoch 57/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 5.7291e-05 - val_loss: 4.9004e-05
Epoch 58/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 6.7873e-05 - val_loss: 6.6344e-04
Epoch 59/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 4.6349e-05 - val_loss: 4.0357e-05
Epoch 60/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 6.7941e-05 - val_loss: 2.5937e-05
Epoch 61/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 3.5275e-05 - val_loss: 3.8480e-05
Epoch 62/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 9.3105e-05 - val_loss: 2.7316e-05
Epoch 63/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 3.0758e-05 - val_loss: 2.5589e-05
Epoch 64/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 4.1119e-05 - val_loss: 2.4874e-05
Epoch 65/1000
3888/3888 [==============================] - 1s 162us/sample - loss: 3.4365e-05 - val_loss: 6.7826e-05
Epoch 66/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 5.0768e-05 - val_loss: 2.5311e-05
Epoch 67/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 4.2197e-05 - val_loss: 2.6397e-04
Epoch 68/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 4.9717e-05 - val_loss: 2.2434e-05
Epoch 69/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 4.6274e-05 - val_loss: 3.1011e-05
Epoch 70/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 5.2399e-05 - val_loss: 2.5650e-05
Epoch 71/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 2.5326e-05 - val_loss: 2.0978e-05
Epoch 72/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 3.6382e-05 - val_loss: 2.2463e-05
Epoch 73/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 5.5984e-05 - val_loss: 2.4758e-05
Epoch 74/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 2.9602e-05 - val_loss: 2.2974e-05
Epoch 75/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 5.0904e-05 - val_loss: 2.1345e-05
Epoch 76/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 2.3756e-05 - val_loss: 2.2046e-05
Epoch 77/1000
3888/3888 [==============================] - 1s 162us/sample - loss: 4.9396e-05 - val_loss: 5.9935e-05
Epoch 78/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 4.3136e-05 - val_loss: 2.0602e-05
Epoch 79/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 2.9133e-05 - val_loss: 2.5864e-04
Epoch 80/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 7.3918e-05 - val_loss: 2.1660e-05
Epoch 81/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 2.1656e-05 - val_loss: 4.1986e-05
Epoch 82/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 3.0388e-05 - val_loss: 2.4040e-05
Epoch 83/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 3.8229e-05 - val_loss: 1.5414e-05
Epoch 84/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 3.3026e-05 - val_loss: 2.1708e-05
Epoch 85/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 2.8399e-05 - val_loss: 9.3783e-05
Epoch 86/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 2.3101e-05 - val_loss: 3.2621e-05
Epoch 87/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 5.1309e-05 - val_loss: 3.1538e-05
Epoch 88/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 1.8559e-05 - val_loss: 6.7896e-05
Epoch 89/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 3.1038e-05 - val_loss: 2.5238e-05
Epoch 90/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 3.9451e-05 - val_loss: 5.2010e-05
Epoch 91/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 3.9412e-05 - val_loss: 1.6047e-05
Epoch 92/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 2.8037e-05 - val_loss: 3.8796e-05
Epoch 93/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 2.2766e-05 - val_loss: 1.5509e-05
Epoch 94/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 2.2778e-05 - val_loss: 2.4701e-05
Epoch 95/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 2.7686e-05 - val_loss: 3.5350e-05
Epoch 96/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 2.3565e-05 - val_loss: 3.4616e-05
Epoch 97/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 3.0037e-05 - val_loss: 5.3302e-05
Epoch 98/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 5.4685e-05 - val_loss: 1.4850e-05
Epoch 99/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 2.0763e-05 - val_loss: 1.8319e-05
Epoch 100/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 1.9349e-05 - val_loss: 1.6575e-05
Epoch 101/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 3.8836e-05 - val_loss: 2.0057e-05
Epoch 102/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 1.5714e-05 - val_loss: 1.5270e-05
Epoch 103/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 1.9804e-05 - val_loss: 3.0410e-05
Epoch 104/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 2.4888e-05 - val_loss: 2.2791e-05
Epoch 105/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 3.9463e-05 - val_loss: 2.6771e-05
Epoch 106/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 2.7890e-05 - val_loss: 1.2698e-05
Epoch 107/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 1.5256e-05 - val_loss: 1.1501e-05
Epoch 108/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 2.7900e-05 - val_loss: 1.1755e-05
Epoch 109/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 3.9958e-05 - val_loss: 1.3093e-05
Epoch 110/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 1.4672e-05 - val_loss: 1.1263e-05
Epoch 111/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 3.2315e-05 - val_loss: 1.3526e-05
Epoch 112/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 2.0585e-05 - val_loss: 2.0821e-05
Epoch 113/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 1.4648e-05 - val_loss: 1.1934e-05
Epoch 114/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 2.9286e-05 - val_loss: 3.2732e-05
Epoch 115/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 1.6027e-05 - val_loss: 2.0702e-05
Epoch 116/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 5.1921e-05 - val_loss: 7.7981e-05
Epoch 117/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 1.5643e-05 - val_loss: 1.1578e-05
Epoch 118/1000
3888/3888 [==============================] - 1s 162us/sample - loss: 1.1226e-05 - val_loss: 1.7894e-05
Epoch 119/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 3.0202e-05 - val_loss: 1.5657e-05
Epoch 120/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 1.2040e-05 - val_loss: 1.2324e-05
Epoch 121/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 2.0070e-05 - val_loss: 2.6288e-04
Epoch 122/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 2.2631e-05 - val_loss: 1.1904e-05
Epoch 123/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 1.7329e-05 - val_loss: 1.5348e-05
Epoch 124/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 2.3928e-05 - val_loss: 1.2670e-05
Epoch 125/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 2.0412e-05 - val_loss: 9.3581e-06
Epoch 126/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 2.1771e-05 - val_loss: 1.8831e-05
Epoch 127/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 2.5334e-05 - val_loss: 4.7775e-05
Epoch 128/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 2.2387e-05 - val_loss: 1.7573e-05
Epoch 129/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 2.6525e-05 - val_loss: 1.2063e-05
Epoch 130/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 1.0571e-05 - val_loss: 1.3839e-05
Epoch 131/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 3.3311e-05 - val_loss: 1.2631e-05
Epoch 132/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 1.2047e-05 - val_loss: 9.7373e-06
Epoch 133/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 1.7530e-05 - val_loss: 3.6506e-05
Epoch 134/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 2.5740e-05 - val_loss: 9.0237e-06
Epoch 135/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 9.9252e-06 - val_loss: 2.2183e-05
Epoch 136/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 3.6348e-05 - val_loss: 1.7399e-05
Epoch 137/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 1.0603e-05 - val_loss: 2.2265e-05
Epoch 138/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 2.2988e-05 - val_loss: 1.6963e-05
Epoch 139/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 1.4670e-05 - val_loss: 1.0630e-05
Epoch 140/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 2.1128e-05 - val_loss: 1.2292e-05
Epoch 141/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 1.5385e-05 - val_loss: 2.9868e-05
Epoch 142/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 1.9851e-05 - val_loss: 7.6551e-06
Epoch 143/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 2.8339e-05 - val_loss: 7.6110e-06
Epoch 144/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 9.5839e-06 - val_loss: 9.5788e-06
Epoch 145/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 2.5811e-05 - val_loss: 8.3010e-06
Epoch 146/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 1.0925e-05 - val_loss: 9.0989e-06
Epoch 147/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 1.8010e-05 - val_loss: 1.3611e-05
Epoch 148/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 2.8578e-05 - val_loss: 6.2824e-05
Epoch 149/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 1.5082e-05 - val_loss: 7.4335e-06
Epoch 150/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 3.2927e-05 - val_loss: 1.1447e-05
Epoch 151/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 8.4998e-06 - val_loss: 9.4952e-06
Epoch 152/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 1.0168e-05 - val_loss: 1.2642e-05
Epoch 153/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 1.8704e-05 - val_loss: 8.0965e-06
Epoch 154/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 1.5729e-05 - val_loss: 6.6933e-05
Epoch 155/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 3.3284e-05 - val_loss: 8.4163e-06
Epoch 156/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 7.5732e-06 - val_loss: 7.9060e-06
Epoch 157/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 1.4612e-05 - val_loss: 7.6637e-06
Epoch 158/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 1.0905e-05 - val_loss: 7.8848e-06
Epoch 159/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 2.0838e-05 - val_loss: 1.2462e-05
Epoch 160/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 1.0337e-05 - val_loss: 6.2857e-05
Epoch 161/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 1.9129e-05 - val_loss: 1.1350e-05
Epoch 162/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 1.3920e-05 - val_loss: 1.4463e-05
Epoch 163/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 1.7938e-05 - val_loss: 6.8174e-06
Epoch 164/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 1.9587e-05 - val_loss: 7.8949e-06
Epoch 165/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 8.5997e-06 - val_loss: 1.5012e-05
Epoch 166/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 6.2674e-05 - val_loss: 6.8490e-06
Epoch 167/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 6.6387e-06 - val_loss: 1.4120e-05
Epoch 168/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 7.4868e-06 - val_loss: 8.3199e-06
Epoch 169/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 7.8242e-06 - val_loss: 8.4830e-06
Epoch 170/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 1.1194e-05 - val_loss: 1.5911e-05
Epoch 171/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 1.2357e-05 - val_loss: 1.3681e-05
Epoch 172/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 1.8332e-05 - val_loss: 9.6728e-06
Epoch 173/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 1.2213e-05 - val_loss: 9.6931e-06
Epoch 174/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 1.3268e-05 - val_loss: 1.3382e-05
Epoch 175/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 3.5114e-05 - val_loss: 2.7774e-05
Epoch 176/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 7.2065e-06 - val_loss: 1.6149e-05
Epoch 177/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 1.4761e-05 - val_loss: 1.2932e-05
Epoch 178/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 8.8781e-06 - val_loss: 1.8651e-05
Epoch 179/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 1.0437e-05 - val_loss: 1.8698e-05
Epoch 180/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 2.2197e-05 - val_loss: 6.6164e-06
Epoch 181/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 9.9575e-06 - val_loss: 8.0228e-06
Epoch 182/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 1.6886e-05 - val_loss: 7.5428e-06
Epoch 183/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 1.0089e-05 - val_loss: 2.7721e-05
Epoch 184/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 1.2796e-05 - val_loss: 6.6397e-06
Epoch 185/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 1.6497e-05 - val_loss: 1.2537e-05
Epoch 186/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 1.1523e-05 - val_loss: 7.4342e-06
Epoch 187/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 2.3104e-05 - val_loss: 7.4411e-06
Epoch 188/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 1.1632e-05 - val_loss: 7.5084e-06
Epoch 189/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 1.4408e-05 - val_loss: 9.8039e-06
Epoch 190/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 1.0078e-05 - val_loss: 4.7622e-05
Epoch 191/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 1.3345e-05 - val_loss: 1.0189e-05
Epoch 192/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 1.5226e-05 - val_loss: 1.3804e-05
Epoch 193/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 1.8407e-05 - val_loss: 6.1693e-06
Epoch 194/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 1.0482e-05 - val_loss: 6.0754e-06
Epoch 195/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 1.7052e-05 - val_loss: 1.0908e-05
Epoch 196/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 1.2090e-05 - val_loss: 6.7758e-06
Epoch 197/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 1.9872e-05 - val_loss: 5.4886e-06
Epoch 198/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 9.1041e-06 - val_loss: 1.6037e-05
Epoch 199/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 3.3477e-05 - val_loss: 7.5185e-06
Epoch 200/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 6.6129e-06 - val_loss: 6.1187e-06
Epoch 201/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 8.3053e-06 - val_loss: 9.7243e-06
Epoch 202/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 9.1907e-06 - val_loss: 5.7320e-06
Epoch 203/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 2.0559e-05 - val_loss: 7.0414e-06
Epoch 204/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 6.3038e-06 - val_loss: 5.5673e-06
Epoch 205/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 1.3877e-05 - val_loss: 5.4835e-06
Epoch 206/1000
3888/3888 [==============================] - 1s 161us/sample - loss: 1.4762e-05 - val_loss: 8.2328e-06
Epoch 207/1000
3888/3888 [==============================] - 1s 162us/sample - loss: 8.8227e-06 - val_loss: 1.1109e-05
Epoch 208/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 1.0193e-05 - val_loss: 1.0757e-05
Epoch 209/1000
3888/3888 [==============================] - 1s 159us/sample - loss: 1.6210e-05 - val_loss: 2.2095e-05
Epoch 210/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 1.0547e-05 - val_loss: 1.2884e-05
Epoch 211/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 1.0618e-05 - val_loss: 1.3064e-04
Epoch 212/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 1.9069e-05 - val_loss: 5.1850e-06
Epoch 213/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 9.1538e-06 - val_loss: 5.5521e-06
Epoch 214/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 2.0057e-05 - val_loss: 7.6671e-06
Epoch 215/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 6.5276e-06 - val_loss: 9.1512e-06
Epoch 216/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 1.5359e-05 - val_loss: 6.9617e-06
Epoch 217/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 1.2081e-05 - val_loss: 5.0828e-06
Epoch 218/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 7.0452e-06 - val_loss: 5.1056e-06
Epoch 219/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 4.5939e-05 - val_loss: 3.0916e-05
Epoch 220/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 7.5386e-06 - val_loss: 1.8468e-05
Epoch 221/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 6.7852e-06 - val_loss: 5.2979e-06
Epoch 222/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 1.1551e-05 - val_loss: 7.2192e-06
Epoch 223/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 1.0054e-05 - val_loss: 9.7108e-06
Epoch 224/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 7.7802e-06 - val_loss: 5.2869e-06
Epoch 225/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 1.3071e-05 - val_loss: 1.2655e-05
Epoch 226/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 8.8989e-06 - val_loss: 1.2160e-05
Epoch 227/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 1.4125e-05 - val_loss: 5.1797e-06
Epoch 228/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 1.0808e-05 - val_loss: 2.1585e-05
Epoch 229/1000
3888/3888 [==============================] - 1s 159us/sample - loss: 1.3656e-05 - val_loss: 1.2228e-05
Epoch 230/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 2.3534e-05 - val_loss: 6.0582e-06
Epoch 231/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 7.3763e-06 - val_loss: 6.1127e-06
Epoch 232/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 1.0561e-05 - val_loss: 6.4672e-06
Epoch 233/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 8.1756e-06 - val_loss: 2.3157e-05
Epoch 234/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 1.0226e-05 - val_loss: 5.3701e-06
Epoch 235/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 1.1181e-05 - val_loss: 6.0515e-06
Epoch 236/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 1.5069e-05 - val_loss: 2.3999e-04
Epoch 237/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 2.8905e-05 - val_loss: 4.2783e-06
Epoch 238/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 5.0404e-06 - val_loss: 4.8141e-06
Epoch 239/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 8.7867e-06 - val_loss: 1.2584e-04
Epoch 240/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 2.0431e-05 - val_loss: 4.4344e-06
Epoch 241/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 4.7971e-06 - val_loss: 1.1865e-05
Epoch 242/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 8.2251e-06 - val_loss: 4.8635e-05
Epoch 243/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 7.8126e-06 - val_loss: 1.9679e-05
Epoch 244/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 1.5146e-05 - val_loss: 5.3746e-06
Epoch 245/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 1.7724e-05 - val_loss: 6.1827e-06
Epoch 246/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 7.7808e-06 - val_loss: 6.7323e-06
Epoch 247/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 8.7133e-06 - val_loss: 5.1471e-06
Epoch 248/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 1.5327e-05 - val_loss: 1.7223e-05
Epoch 249/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 9.5306e-06 - val_loss: 9.6417e-05
Epoch 250/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 2.4026e-05 - val_loss: 5.1316e-06
Epoch 251/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 5.3344e-06 - val_loss: 3.5736e-06
Epoch 252/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 4.9748e-06 - val_loss: 4.2490e-06
Epoch 253/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 9.9845e-06 - val_loss: 4.9721e-06
Epoch 254/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 9.4673e-06 - val_loss: 8.0273e-05
Epoch 255/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 8.8333e-06 - val_loss: 4.5998e-06
Epoch 256/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 9.8041e-06 - val_loss: 3.0341e-05
Epoch 257/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 2.1863e-05 - val_loss: 2.3186e-05
Epoch 258/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 1.1555e-05 - val_loss: 5.4273e-06
Epoch 259/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 5.1461e-06 - val_loss: 5.3083e-06
Epoch 260/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 1.2189e-05 - val_loss: 4.7553e-06
Epoch 261/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 8.7927e-06 - val_loss: 6.1851e-06
Epoch 262/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 1.0477e-05 - val_loss: 5.3305e-06
Epoch 263/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 1.0374e-05 - val_loss: 6.2210e-06
Epoch 264/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 1.6051e-05 - val_loss: 1.0271e-05
Epoch 265/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 1.1601e-05 - val_loss: 4.6802e-06
Epoch 266/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 1.3733e-05 - val_loss: 4.6581e-06
Epoch 267/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 6.4845e-06 - val_loss: 7.0475e-06
Epoch 268/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 1.0336e-05 - val_loss: 1.4583e-05
Epoch 269/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 7.9788e-06 - val_loss: 1.2923e-04
Epoch 270/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 1.4877e-05 - val_loss: 4.0521e-06
Epoch 271/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 2.7689e-05 - val_loss: 5.7481e-06
Epoch 272/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 4.6589e-06 - val_loss: 4.3783e-06
Epoch 273/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 5.9236e-06 - val_loss: 4.5348e-05
Epoch 274/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 1.0428e-05 - val_loss: 8.1480e-06
Epoch 275/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 9.4357e-06 - val_loss: 9.4461e-06
Epoch 276/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 1.7846e-05 - val_loss: 1.1419e-05
Epoch 277/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 6.7671e-06 - val_loss: 6.2370e-06
Epoch 278/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 5.4216e-06 - val_loss: 4.7033e-06
Epoch 279/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 6.4102e-06 - val_loss: 6.3642e-06
Epoch 280/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 2.2950e-05 - val_loss: 9.8561e-06
Epoch 281/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 6.4483e-06 - val_loss: 5.0064e-06
Epoch 282/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 1.0415e-05 - val_loss: 3.8922e-06
Epoch 283/1000
3888/3888 [==============================] - 1s 157us/sample - loss: 7.3388e-06 - val_loss: 2.8215e-05
Epoch 284/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 1.9822e-05 - val_loss: 4.6206e-06
Epoch 285/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 8.4450e-06 - val_loss: 6.4214e-06
Epoch 286/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 5.0932e-06 - val_loss: 9.2331e-06
Epoch 287/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 1.1163e-05 - val_loss: 3.6472e-06
Epoch 288/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 1.8076e-05 - val_loss: 1.8491e-05
Epoch 289/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 6.2439e-06 - val_loss: 2.0756e-05
Epoch 290/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 8.1149e-06 - val_loss: 5.9633e-06
Epoch 291/1000
3888/3888 [==============================] - 1s 162us/sample - loss: 1.0015e-05 - val_loss: 8.8248e-06
Epoch 292/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 1.0581e-05 - val_loss: 7.4719e-06
Epoch 293/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 9.3946e-06 - val_loss: 1.1636e-04
Epoch 294/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 1.3211e-05 - val_loss: 5.5047e-06
Epoch 295/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 1.3807e-05 - val_loss: 1.3238e-05
Epoch 296/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 1.7157e-05 - val_loss: 1.2660e-05
Epoch 297/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 5.3122e-06 - val_loss: 5.9720e-06
Epoch 298/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 5.9270e-06 - val_loss: 5.7431e-06
Epoch 299/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 1.6391e-05 - val_loss: 1.0898e-05
Epoch 300/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 5.5745e-06 - val_loss: 3.5126e-06
Epoch 301/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 1.4149e-05 - val_loss: 2.5050e-05
Epoch 302/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 1.1808e-05 - val_loss: 4.6985e-06
Epoch 303/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 8.0499e-06 - val_loss: 3.0485e-05
Epoch 304/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 1.1508e-05 - val_loss: 5.7677e-06
Epoch 305/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 1.4347e-05 - val_loss: 4.1689e-06
Epoch 306/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 4.5567e-06 - val_loss: 1.1056e-05
Epoch 307/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 7.4921e-06 - val_loss: 3.2717e-06
Epoch 308/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 2.1503e-05 - val_loss: 4.2534e-06
Epoch 309/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 5.5902e-06 - val_loss: 7.8330e-06
Epoch 310/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 5.3964e-06 - val_loss: 4.3555e-06
Epoch 311/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 3.2975e-05 - val_loss: 1.5889e-05
Epoch 312/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 4.2725e-06 - val_loss: 3.4147e-06
Epoch 313/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 5.9557e-06 - val_loss: 5.5331e-06
Epoch 314/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 6.0954e-06 - val_loss: 4.6023e-06
Epoch 315/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 7.2711e-06 - val_loss: 1.2843e-05
Epoch 316/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 6.0468e-06 - val_loss: 5.5753e-06
Epoch 317/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 1.2357e-05 - val_loss: 1.0158e-05
Epoch 318/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 7.8644e-06 - val_loss: 3.6753e-06
Epoch 319/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 1.1166e-05 - val_loss: 1.6105e-05
Epoch 320/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 6.7154e-06 - val_loss: 4.0704e-06
Epoch 321/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 1.1132e-05 - val_loss: 5.2871e-06
Epoch 322/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 9.2533e-06 - val_loss: 7.7238e-06
Epoch 323/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 2.4442e-05 - val_loss: 5.3351e-06
Epoch 324/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 4.4400e-06 - val_loss: 4.7407e-06
Epoch 325/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 5.3865e-06 - val_loss: 4.7225e-06
Epoch 326/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 8.6586e-06 - val_loss: 5.8643e-06
Epoch 327/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 1.8807e-05 - val_loss: 6.0742e-06
Epoch 328/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 5.9499e-06 - val_loss: 1.9017e-05
Epoch 329/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 5.5070e-06 - val_loss: 1.0281e-05
Epoch 330/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 1.3545e-05 - val_loss: 4.3046e-06
Epoch 331/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 4.6358e-06 - val_loss: 7.7832e-06
Epoch 332/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 1.2000e-05 - val_loss: 3.9405e-06
Epoch 333/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 5.3208e-06 - val_loss: 3.6070e-06
Epoch 334/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 1.1551e-05 - val_loss: 5.1382e-06
Epoch 335/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 1.8319e-05 - val_loss: 4.1359e-04
Epoch 336/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 2.1675e-05 - val_loss: 3.3741e-06
Epoch 337/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 3.7597e-06 - val_loss: 4.8394e-06
Epoch 338/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 4.9801e-06 - val_loss: 9.6883e-06
Epoch 339/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 7.5459e-06 - val_loss: 5.6855e-06
Epoch 340/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 1.1239e-05 - val_loss: 4.5398e-06
Epoch 341/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 7.5917e-06 - val_loss: 3.9320e-06
Epoch 342/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 9.7760e-06 - val_loss: 2.3736e-04
Epoch 343/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 8.0052e-06 - val_loss: 2.9829e-06
Epoch 344/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 1.0756e-05 - val_loss: 2.9324e-06
Epoch 345/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 2.2911e-05 - val_loss: 1.4155e-05
Epoch 346/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 4.0821e-06 - val_loss: 3.7546e-06
Epoch 347/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 6.4453e-06 - val_loss: 3.6433e-06
Epoch 348/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 6.6838e-06 - val_loss: 9.2481e-05
Epoch 349/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 7.7622e-06 - val_loss: 3.6064e-06
Epoch 350/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 8.4285e-06 - val_loss: 2.6588e-05
Epoch 351/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 1.1593e-05 - val_loss: 3.9792e-06
Epoch 352/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 1.3230e-05 - val_loss: 5.6334e-06
Epoch 353/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 5.7762e-06 - val_loss: 3.5595e-06
Epoch 354/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 6.2687e-06 - val_loss: 7.4060e-06
Epoch 355/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 1.0162e-05 - val_loss: 1.0944e-05
Epoch 356/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 2.7119e-05 - val_loss: 5.0458e-06
Epoch 357/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 3.9935e-06 - val_loss: 3.6135e-06
Epoch 358/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 5.4689e-06 - val_loss: 4.4243e-06
Epoch 359/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 8.9716e-06 - val_loss: 3.2339e-05
Epoch 360/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 1.0171e-05 - val_loss: 5.6580e-06
Epoch 361/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 8.6792e-06 - val_loss: 1.2932e-05
Epoch 362/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 5.1457e-06 - val_loss: 5.4555e-06
Epoch 363/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 8.6839e-06 - val_loss: 4.0269e-06
Epoch 364/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 5.8739e-05 - val_loss: 4.0364e-06
Epoch 365/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 2.9905e-06 - val_loss: 2.7873e-06
Epoch 366/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 2.9259e-06 - val_loss: 2.6409e-06
Epoch 367/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 3.5596e-06 - val_loss: 1.6126e-05
Epoch 368/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 1.2055e-05 - val_loss: 6.3265e-06
Epoch 369/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 5.1508e-06 - val_loss: 4.7824e-06
Epoch 370/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 1.2494e-05 - val_loss: 4.8266e-06
Epoch 371/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 2.9883e-06 - val_loss: 3.4817e-06
Epoch 372/1000
3888/3888 [==============================] - 1s 179us/sample - loss: 3.5770e-06 - val_loss: 3.2407e-06
Epoch 373/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 9.9641e-06 - val_loss: 3.9142e-06
Epoch 374/1000
3888/3888 [==============================] - 1s 178us/sample - loss: 4.4466e-06 - val_loss: 1.0860e-05
Epoch 375/1000
3888/3888 [==============================] - 1s 178us/sample - loss: 1.5610e-05 - val_loss: 5.2315e-06
Epoch 376/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 4.1728e-06 - val_loss: 3.7117e-06
Epoch 377/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 7.6357e-06 - val_loss: 3.1505e-06
Epoch 378/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 7.7787e-06 - val_loss: 6.8963e-06
Epoch 379/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 1.1418e-05 - val_loss: 3.6732e-05
Epoch 380/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 1.2511e-05 - val_loss: 2.3034e-05
Epoch 381/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 5.6154e-06 - val_loss: 9.4374e-06
Epoch 382/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 8.7741e-06 - val_loss: 3.5170e-06
Epoch 383/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 2.9549e-05 - val_loss: 1.2875e-05
Epoch 384/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 3.6103e-06 - val_loss: 2.6301e-06
Epoch 385/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 3.4067e-06 - val_loss: 5.3911e-06
Epoch 386/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 5.1253e-06 - val_loss: 4.5703e-06
Epoch 387/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 8.5378e-06 - val_loss: 6.4900e-05
Epoch 388/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 1.4220e-05 - val_loss: 3.5075e-06
Epoch 389/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 3.6732e-06 - val_loss: 3.4816e-06
Epoch 390/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 7.8688e-06 - val_loss: 3.3996e-06
Epoch 391/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 7.2387e-06 - val_loss: 2.7627e-06
Epoch 392/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 9.7411e-06 - val_loss: 5.9485e-06
Epoch 393/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 9.2277e-06 - val_loss: 3.3208e-06
Epoch 394/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 1.3487e-05 - val_loss: 3.4789e-06
Epoch 395/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 6.8777e-06 - val_loss: 8.1317e-06
Epoch 396/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 2.5382e-05 - val_loss: 2.7258e-05
Epoch 397/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 4.9436e-06 - val_loss: 3.2157e-06
Epoch 398/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 3.3265e-06 - val_loss: 3.2238e-06
Epoch 399/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 6.2774e-06 - val_loss: 3.1650e-06
Epoch 400/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 7.5438e-06 - val_loss: 1.2491e-05
Epoch 401/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 8.4453e-06 - val_loss: 3.9355e-06
Epoch 402/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 5.2062e-06 - val_loss: 5.7513e-06
Epoch 403/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 9.6880e-06 - val_loss: 1.5079e-05
Epoch 404/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 1.2819e-05 - val_loss: 2.9499e-06
Epoch 405/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 4.9639e-06 - val_loss: 3.8571e-06
Epoch 406/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 3.4410e-05 - val_loss: 3.9061e-06
Epoch 407/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 3.1977e-06 - val_loss: 4.5615e-06
Epoch 408/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 6.6595e-06 - val_loss: 2.6380e-06
Epoch 409/1000
3888/3888 [==============================] - 1s 179us/sample - loss: 3.9393e-06 - val_loss: 4.3143e-05
Epoch 410/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 8.7277e-06 - val_loss: 3.6748e-06
Epoch 411/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 6.0651e-06 - val_loss: 1.1370e-05
Epoch 412/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 5.6923e-06 - val_loss: 8.1826e-06
Epoch 413/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 8.7038e-06 - val_loss: 3.1376e-06
Epoch 414/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 1.9015e-05 - val_loss: 5.0624e-06
Epoch 415/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 3.9656e-06 - val_loss: 2.2221e-05
Epoch 416/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 9.3168e-06 - val_loss: 7.6860e-06
Epoch 417/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 1.9901e-05 - val_loss: 3.5244e-06
Epoch 418/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 3.1827e-06 - val_loss: 3.2904e-06
Epoch 419/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 4.8657e-06 - val_loss: 1.0701e-05
Epoch 420/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 5.7142e-06 - val_loss: 3.1441e-06
Epoch 421/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 1.0619e-05 - val_loss: 3.3862e-06
Epoch 422/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 4.3337e-06 - val_loss: 3.6045e-06
Epoch 423/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 1.1331e-05 - val_loss: 1.8343e-05
Epoch 424/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 2.2488e-05 - val_loss: 4.6157e-06
Epoch 425/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 3.0622e-06 - val_loss: 8.2734e-06
Epoch 426/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 3.6168e-06 - val_loss: 4.3664e-06
Epoch 427/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 6.3592e-06 - val_loss: 1.1169e-05
Epoch 428/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 1.6308e-05 - val_loss: 4.4296e-05
Epoch 429/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 4.4065e-06 - val_loss: 3.2271e-05
Epoch 430/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 5.4491e-06 - val_loss: 2.5584e-06
Epoch 431/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 8.9666e-06 - val_loss: 7.0473e-06
Epoch 432/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 8.6746e-06 - val_loss: 5.7882e-06
Epoch 433/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 9.4038e-06 - val_loss: 4.3911e-06
Epoch 434/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 4.8378e-06 - val_loss: 1.0350e-05
Epoch 435/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 8.5769e-06 - val_loss: 7.5321e-06
Epoch 436/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 1.2446e-05 - val_loss: 7.5101e-06
Epoch 437/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 1.1076e-05 - val_loss: 4.2544e-06
Epoch 438/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 4.4073e-06 - val_loss: 9.7141e-06
Epoch 439/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 2.1809e-05 - val_loss: 4.1786e-06
Epoch 440/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 3.5189e-06 - val_loss: 4.6077e-06
Epoch 441/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 4.0842e-06 - val_loss: 3.7683e-06
Epoch 442/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 5.0600e-06 - val_loss: 1.6969e-05
Epoch 443/1000
3888/3888 [==============================] - 1s 176us/sample - loss: 1.0210e-05 - val_loss: 7.5419e-06
Epoch 444/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 9.4297e-06 - val_loss: 3.3546e-06
Epoch 445/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 8.4666e-06 - val_loss: 1.2456e-05
Epoch 446/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 1.0003e-05 - val_loss: 1.4192e-05
Epoch 447/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 6.1268e-06 - val_loss: 5.0171e-06
Epoch 448/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 1.0346e-05 - val_loss: 3.0288e-06
Epoch 449/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 1.0833e-05 - val_loss: 4.2388e-06
Epoch 450/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 3.7321e-06 - val_loss: 7.5831e-06
Epoch 451/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 9.6491e-06 - val_loss: 4.3831e-06
Epoch 452/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 1.3097e-05 - val_loss: 3.3071e-06
Epoch 453/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 5.0755e-06 - val_loss: 2.9045e-06
Epoch 454/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 5.7364e-06 - val_loss: 1.6519e-04
Epoch 455/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 1.6471e-05 - val_loss: 3.3185e-06
Epoch 456/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 6.3478e-06 - val_loss: 3.1858e-06
Epoch 457/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 1.5151e-05 - val_loss: 6.1860e-06
Epoch 458/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 4.1909e-06 - val_loss: 2.5943e-06
Epoch 459/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 6.5814e-06 - val_loss: 2.5300e-06
Epoch 460/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 4.0121e-06 - val_loss: 5.2197e-06
Epoch 461/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 7.6334e-06 - val_loss: 3.1797e-06
Epoch 462/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 1.2220e-05 - val_loss: 2.4944e-06
Epoch 463/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 6.5070e-06 - val_loss: 5.0679e-06
Epoch 464/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 4.0023e-06 - val_loss: 3.6424e-06
Epoch 465/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 8.2103e-06 - val_loss: 7.8455e-06
Epoch 466/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 1.2903e-05 - val_loss: 4.2873e-06
Epoch 467/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 4.9283e-06 - val_loss: 6.8694e-06
Epoch 468/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 2.3989e-05 - val_loss: 2.4157e-06
Epoch 469/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 2.9605e-06 - val_loss: 2.3979e-06
Epoch 470/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 4.5422e-06 - val_loss: 2.5331e-06
Epoch 471/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 1.0753e-05 - val_loss: 4.2875e-06
Epoch 472/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 5.1282e-06 - val_loss: 5.3128e-06
Epoch 473/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 1.8463e-05 - val_loss: 5.5651e-06
Epoch 474/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 3.6825e-06 - val_loss: 3.9530e-06
Epoch 475/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 4.4487e-06 - val_loss: 2.8871e-06
Epoch 476/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 7.1087e-06 - val_loss: 3.5271e-06
Epoch 477/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 6.6146e-06 - val_loss: 3.2352e-06
Epoch 478/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 1.3658e-05 - val_loss: 5.3646e-06
Epoch 479/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 4.7163e-06 - val_loss: 3.0681e-06
Epoch 480/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 3.5382e-06 - val_loss: 4.4004e-06
Epoch 481/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 1.8956e-05 - val_loss: 5.7961e-06
Epoch 482/1000
3888/3888 [==============================] - 1s 162us/sample - loss: 3.9107e-06 - val_loss: 8.8000e-06
Epoch 483/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 5.4816e-06 - val_loss: 3.2461e-06
Epoch 484/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 6.2144e-06 - val_loss: 5.8745e-06
Epoch 485/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 2.0546e-05 - val_loss: 2.2743e-06
Epoch 486/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 2.6837e-06 - val_loss: 4.7397e-06
Epoch 487/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 3.5805e-06 - val_loss: 3.7137e-06
Epoch 488/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 8.3241e-06 - val_loss: 6.6537e-05
Epoch 489/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 1.0601e-05 - val_loss: 2.2468e-06
Epoch 490/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 3.4153e-06 - val_loss: 3.3736e-06
Epoch 491/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 1.5456e-05 - val_loss: 3.6638e-06
Epoch 492/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 5.8104e-06 - val_loss: 9.0092e-06
Epoch 493/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 6.0428e-06 - val_loss: 5.4377e-06
Epoch 494/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 1.4788e-05 - val_loss: 2.6950e-06
Epoch 495/1000
3888/3888 [==============================] - 1s 161us/sample - loss: 3.6352e-06 - val_loss: 2.4965e-06
Epoch 496/1000
3888/3888 [==============================] - 1s 162us/sample - loss: 6.6340e-06 - val_loss: 2.2906e-06
Epoch 497/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 8.1215e-06 - val_loss: 4.0459e-06
Epoch 498/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 1.0471e-05 - val_loss: 3.3839e-06
Epoch 499/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 7.0745e-06 - val_loss: 3.0207e-06
Epoch 500/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 4.8430e-06 - val_loss: 2.7745e-06
Epoch 501/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 9.8741e-06 - val_loss: 3.5460e-06
Epoch 502/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 5.6537e-06 - val_loss: 2.8081e-06
Epoch 503/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 1.0974e-05 - val_loss: 3.2872e-06
Epoch 504/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 4.8955e-06 - val_loss: 8.9933e-06
Epoch 505/1000
3888/3888 [==============================] - 1s 162us/sample - loss: 1.4767e-05 - val_loss: 4.4001e-06
Epoch 506/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 3.8573e-06 - val_loss: 2.9806e-06
Epoch 507/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 7.0499e-06 - val_loss: 3.7876e-06
Epoch 508/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 6.3418e-06 - val_loss: 2.6856e-06
Epoch 509/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 9.7561e-06 - val_loss: 9.4345e-05
Epoch 510/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 1.4799e-05 - val_loss: 2.6547e-06
Epoch 511/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 1.0276e-05 - val_loss: 2.6792e-06
Epoch 512/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 3.6023e-06 - val_loss: 3.2308e-06
Epoch 513/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 4.8425e-06 - val_loss: 1.8339e-05
Epoch 514/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 3.5220e-05 - val_loss: 6.7517e-06
Epoch 515/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 2.7801e-06 - val_loss: 2.6363e-06
Epoch 516/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 2.3591e-06 - val_loss: 2.6514e-06
Epoch 517/1000
3888/3888 [==============================] - 1s 161us/sample - loss: 4.0640e-06 - val_loss: 2.2862e-06
Epoch 518/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 4.3175e-06 - val_loss: 2.9472e-06
Epoch 519/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 1.5639e-05 - val_loss: 2.5745e-06
Epoch 520/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 2.8680e-06 - val_loss: 2.6642e-06
Epoch 521/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 3.1189e-06 - val_loss: 3.6216e-06
Epoch 522/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 9.7338e-06 - val_loss: 2.3382e-06
Epoch 523/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 9.5729e-06 - val_loss: 5.4297e-06
Epoch 524/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 3.7782e-06 - val_loss: 2.9875e-06
Epoch 525/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 5.0834e-06 - val_loss: 4.2689e-06
Epoch 526/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 9.5475e-06 - val_loss: 4.4690e-05
Epoch 527/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 6.0988e-06 - val_loss: 9.9161e-06
Epoch 528/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 9.9685e-06 - val_loss: 4.0761e-06
Epoch 529/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 2.7720e-06 - val_loss: 4.3785e-06
Epoch 530/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 1.1965e-05 - val_loss: 2.0524e-05
Epoch 531/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 6.9731e-06 - val_loss: 5.0076e-04
Epoch 532/1000
3888/3888 [==============================] - 1s 161us/sample - loss: 1.7071e-05 - val_loss: 5.1372e-06
Epoch 533/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 2.8901e-06 - val_loss: 1.1619e-05
Epoch 534/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 1.0359e-05 - val_loss: 8.2168e-06
Epoch 535/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 3.8009e-06 - val_loss: 2.0494e-06
Epoch 536/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 4.4112e-06 - val_loss: 1.7928e-05
Epoch 537/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 6.3608e-06 - val_loss: 5.3772e-06
Epoch 538/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 1.1524e-05 - val_loss: 1.6460e-05
Epoch 539/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 9.1070e-06 - val_loss: 2.4266e-06
Epoch 540/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 4.7735e-06 - val_loss: 3.1909e-06
Epoch 541/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 5.9909e-06 - val_loss: 2.9661e-04
Epoch 542/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 1.6526e-05 - val_loss: 3.1603e-06
Epoch 543/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 3.8250e-06 - val_loss: 9.4982e-06
Epoch 544/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 9.7077e-06 - val_loss: 6.6249e-06
Epoch 545/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 3.6057e-06 - val_loss: 1.6924e-05
Epoch 546/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 4.9536e-06 - val_loss: 8.7292e-06
Epoch 547/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 1.2411e-05 - val_loss: 9.0507e-06
Epoch 548/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 4.7158e-06 - val_loss: 3.6979e-06
Epoch 549/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 7.8583e-06 - val_loss: 3.5711e-05
Epoch 550/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 6.6038e-06 - val_loss: 2.7543e-06
Epoch 551/1000
3888/3888 [==============================] - 1s 162us/sample - loss: 3.8270e-06 - val_loss: 3.0933e-05
Epoch 552/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 1.3328e-05 - val_loss: 6.3377e-06
Epoch 553/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 5.7365e-06 - val_loss: 3.5911e-06
Epoch 554/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 5.4481e-06 - val_loss: 2.3295e-05
Epoch 555/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 1.9009e-05 - val_loss: 5.3621e-06
Epoch 556/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 3.6942e-06 - val_loss: 4.2823e-06
Epoch 557/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 5.6321e-06 - val_loss: 2.9906e-06
Epoch 558/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 4.9592e-06 - val_loss: 3.2649e-06
Epoch 559/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 1.1233e-05 - val_loss: 3.9317e-06
Epoch 560/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 8.3850e-06 - val_loss: 4.1759e-06
Epoch 561/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 3.1517e-06 - val_loss: 2.4860e-06
Epoch 562/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 8.5320e-06 - val_loss: 3.1471e-06
Epoch 563/1000
3888/3888 [==============================] - 1s 162us/sample - loss: 6.0377e-06 - val_loss: 4.1571e-06
Epoch 564/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 5.1724e-06 - val_loss: 2.2032e-06
Epoch 565/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 1.7054e-05 - val_loss: 7.4169e-06
Epoch 566/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 4.4974e-06 - val_loss: 2.2363e-06
Epoch 567/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 3.7101e-06 - val_loss: 4.5144e-06
Epoch 568/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 5.7443e-06 - val_loss: 2.3940e-06
Epoch 569/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 1.8161e-05 - val_loss: 2.7057e-06
Epoch 570/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 7.3577e-06 - val_loss: 2.8099e-06
Epoch 571/1000
3888/3888 [==============================] - 1s 162us/sample - loss: 2.8868e-06 - val_loss: 5.6265e-06
Epoch 572/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 1.4986e-05 - val_loss: 2.0325e-06
Epoch 573/1000
3888/3888 [==============================] - 1s 162us/sample - loss: 2.6073e-06 - val_loss: 2.9666e-06
Epoch 574/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 4.8887e-06 - val_loss: 4.5774e-05
Epoch 575/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 6.3607e-06 - val_loss: 4.2190e-06
Epoch 576/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 4.3793e-06 - val_loss: 4.3023e-06
Epoch 577/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 5.6023e-06 - val_loss: 2.6898e-06
Epoch 578/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 4.3601e-05 - val_loss: 2.1711e-06
Epoch 579/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 2.2702e-06 - val_loss: 3.4386e-06
Epoch 580/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 2.3803e-06 - val_loss: 2.6804e-06
Epoch 581/1000
3888/3888 [==============================] - 1s 161us/sample - loss: 3.6102e-06 - val_loss: 2.6324e-06
Epoch 582/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 4.1063e-06 - val_loss: 3.0626e-06
Epoch 583/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 5.8641e-06 - val_loss: 3.8579e-06
Epoch 584/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 8.3281e-06 - val_loss: 2.6185e-06
Epoch 585/1000
3888/3888 [==============================] - 1s 162us/sample - loss: 3.3884e-06 - val_loss: 3.7735e-06
Epoch 586/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 9.5669e-06 - val_loss: 2.8033e-06
Epoch 587/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 2.6300e-06 - val_loss: 7.1490e-06
Epoch 588/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 8.1629e-06 - val_loss: 3.3123e-06
Epoch 589/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 6.3849e-06 - val_loss: 2.2863e-05
Epoch 590/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 5.9293e-06 - val_loss: 2.5432e-06
Epoch 591/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 7.6427e-06 - val_loss: 4.2926e-06
Epoch 592/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 6.9737e-06 - val_loss: 1.6732e-05
Epoch 593/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 7.0126e-06 - val_loss: 1.6030e-05
Epoch 594/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 8.4753e-06 - val_loss: 2.9083e-06
Epoch 595/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 4.3914e-06 - val_loss: 9.0456e-06
Epoch 596/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 8.4843e-06 - val_loss: 9.1891e-05
Epoch 597/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 1.2993e-05 - val_loss: 2.5238e-06
Epoch 598/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 4.0747e-06 - val_loss: 3.6334e-06
Epoch 599/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 2.8808e-06 - val_loss: 9.1319e-05
Epoch 600/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 1.0723e-05 - val_loss: 2.4317e-06
Epoch 601/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 4.2033e-06 - val_loss: 4.2531e-06
Epoch 602/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 1.1684e-05 - val_loss: 1.7976e-05
Epoch 603/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 4.9644e-06 - val_loss: 2.3510e-06
Epoch 604/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 1.7155e-05 - val_loss: 3.3066e-06
Epoch 605/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 2.2901e-06 - val_loss: 4.9084e-06
Epoch 606/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 6.9689e-06 - val_loss: 2.2229e-06
Epoch 607/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 4.4699e-06 - val_loss: 9.7948e-06
Epoch 608/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 3.4524e-06 - val_loss: 1.2243e-05
Epoch 609/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 5.5679e-06 - val_loss: 7.9777e-06
Epoch 610/1000
3888/3888 [==============================] - 1s 162us/sample - loss: 1.0225e-05 - val_loss: 2.5243e-06
Epoch 611/1000
3888/3888 [==============================] - 1s 159us/sample - loss: 5.9230e-06 - val_loss: 1.2367e-05
Epoch 612/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 8.1973e-06 - val_loss: 5.2115e-06
Epoch 613/1000
3888/3888 [==============================] - 1s 162us/sample - loss: 1.0981e-05 - val_loss: 4.1662e-04
Epoch 614/1000
3888/3888 [==============================] - 1s 162us/sample - loss: 1.0291e-05 - val_loss: 2.3225e-06
Epoch 615/1000
3888/3888 [==============================] - 1s 161us/sample - loss: 2.5623e-06 - val_loss: 2.4138e-06
Epoch 616/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 6.4597e-06 - val_loss: 2.4034e-05
Epoch 617/1000
3888/3888 [==============================] - 1s 158us/sample - loss: 1.0837e-05 - val_loss: 2.4497e-06
Epoch 618/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 4.4676e-06 - val_loss: 5.3235e-06
Epoch 619/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 3.6024e-06 - val_loss: 3.0106e-06
Epoch 620/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 8.5510e-06 - val_loss: 2.0851e-06
Epoch 621/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 5.5331e-06 - val_loss: 7.1334e-06
Epoch 622/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 1.1532e-05 - val_loss: 1.0257e-05
Epoch 623/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 4.6361e-06 - val_loss: 2.2319e-06
Epoch 624/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 4.8293e-06 - val_loss: 2.1060e-06
Epoch 625/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 5.2566e-06 - val_loss: 2.8273e-06
Epoch 626/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 2.3481e-05 - val_loss: 2.8198e-06
Epoch 627/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 2.4382e-06 - val_loss: 2.7181e-06
Epoch 628/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 6.7636e-06 - val_loss: 2.9671e-06
Epoch 629/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 2.8254e-06 - val_loss: 2.9675e-06
Epoch 630/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 3.7969e-06 - val_loss: 2.3639e-06
Epoch 631/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 7.2364e-06 - val_loss: 2.4838e-06
Epoch 632/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 5.7791e-06 - val_loss: 3.1207e-06
Epoch 633/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 9.9815e-06 - val_loss: 2.3000e-06
Epoch 634/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 5.4541e-06 - val_loss: 2.4982e-06
Epoch 635/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 6.8936e-06 - val_loss: 2.0878e-06
Epoch 636/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 4.5526e-06 - val_loss: 2.1262e-06
Epoch 637/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 8.9842e-06 - val_loss: 2.7301e-06
Epoch 638/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 6.2820e-06 - val_loss: 7.1352e-06
Epoch 639/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 1.5106e-05 - val_loss: 3.7374e-05
Epoch 640/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 3.4692e-06 - val_loss: 2.5835e-06
Epoch 641/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 4.8777e-06 - val_loss: 1.9721e-06
Epoch 642/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 3.8143e-06 - val_loss: 3.7580e-06
Epoch 643/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 5.4087e-06 - val_loss: 1.8463e-06
Epoch 644/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 1.7205e-05 - val_loss: 2.2203e-06
Epoch 645/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 2.6050e-06 - val_loss: 2.2152e-06
Epoch 646/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 5.3518e-06 - val_loss: 8.0691e-06
Epoch 647/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 1.8656e-05 - val_loss: 2.5127e-06
Epoch 648/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 2.1149e-06 - val_loss: 3.2720e-06
Epoch 649/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 2.4947e-06 - val_loss: 3.1246e-06
Epoch 650/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 5.4091e-06 - val_loss: 1.9420e-06
Epoch 651/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 9.9869e-06 - val_loss: 6.0130e-06
Epoch 652/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 6.2578e-06 - val_loss: 4.0553e-05
Epoch 653/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 6.5127e-06 - val_loss: 2.4521e-06
Epoch 654/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 5.8958e-06 - val_loss: 1.9295e-06
Epoch 655/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 1.1211e-05 - val_loss: 2.9413e-05
Epoch 656/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 3.5038e-06 - val_loss: 2.9672e-06
Epoch 657/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 6.7257e-06 - val_loss: 2.7937e-06
Epoch 658/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 9.6235e-06 - val_loss: 6.0334e-06
Epoch 659/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 4.2697e-06 - val_loss: 2.7478e-06
Epoch 660/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 1.2434e-05 - val_loss: 2.0820e-05
Epoch 661/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 4.3218e-06 - val_loss: 4.3094e-05
Epoch 662/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 3.8367e-06 - val_loss: 7.1007e-06
Epoch 663/1000
3888/3888 [==============================] - 1s 153us/sample - loss: 8.0818e-06 - val_loss: 6.6116e-06
Epoch 664/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 7.2446e-06 - val_loss: 4.3292e-06
Epoch 665/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 2.7070e-06 - val_loss: 1.0022e-05
Epoch 666/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 8.0687e-06 - val_loss: 2.3745e-05
Epoch 667/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 1.3892e-05 - val_loss: 5.5405e-06
Epoch 668/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 2.9379e-06 - val_loss: 7.2324e-06
Epoch 669/1000
3888/3888 [==============================] - 1s 175us/sample - loss: 7.1673e-06 - val_loss: 2.9749e-06
Epoch 670/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 4.2551e-06 - val_loss: 3.6768e-06
Epoch 671/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 4.5692e-06 - val_loss: 1.4716e-05
Epoch 672/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 9.2295e-06 - val_loss: 2.6240e-06
Epoch 673/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 1.2107e-05 - val_loss: 1.8759e-06
Epoch 674/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 9.5258e-06 - val_loss: 5.5561e-05
Epoch 675/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 7.0844e-06 - val_loss: 1.9868e-06
Epoch 676/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 2.4193e-06 - val_loss: 5.3140e-06
Epoch 677/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 5.5394e-06 - val_loss: 2.0907e-06
Epoch 678/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 5.0830e-06 - val_loss: 1.9128e-05
Epoch 679/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 1.0754e-05 - val_loss: 1.8111e-06
Epoch 680/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 3.6430e-06 - val_loss: 4.8077e-06
Epoch 681/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 1.2270e-05 - val_loss: 3.3499e-06
Epoch 682/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 3.4881e-06 - val_loss: 3.5932e-06
Epoch 683/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 8.7650e-06 - val_loss: 8.9997e-06
Epoch 684/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 3.2323e-06 - val_loss: 2.5675e-06
Epoch 685/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 1.3130e-05 - val_loss: 5.0054e-06
Epoch 686/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 2.1298e-06 - val_loss: 1.7983e-06
Epoch 687/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 4.2279e-06 - val_loss: 2.6224e-06
Epoch 688/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 1.0000e-05 - val_loss: 1.1273e-05
Epoch 689/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 8.7483e-06 - val_loss: 2.3760e-06
Epoch 690/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 5.0569e-06 - val_loss: 1.9603e-04
Epoch 691/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 6.3105e-06 - val_loss: 1.9406e-06
Epoch 692/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 2.6690e-06 - val_loss: 4.9213e-06
Epoch 693/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 6.4126e-06 - val_loss: 5.5727e-06
Epoch 694/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 8.7468e-06 - val_loss: 2.6840e-06
Epoch 695/1000
3888/3888 [==============================] - 1s 161us/sample - loss: 6.7808e-06 - val_loss: 1.6310e-04
Epoch 696/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 1.1829e-05 - val_loss: 3.1340e-06
Epoch 697/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 2.4261e-06 - val_loss: 2.6237e-06
Epoch 698/1000
3888/3888 [==============================] - 1s 160us/sample - loss: 3.5702e-06 - val_loss: 1.9178e-06
Epoch 699/1000
3888/3888 [==============================] - 1s 160us/sample - loss: 7.4502e-06 - val_loss: 3.7225e-06
Epoch 700/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 7.0083e-06 - val_loss: 1.2410e-05
Epoch 701/1000
3888/3888 [==============================] - 1s 160us/sample - loss: 1.1793e-05 - val_loss: 3.3129e-06
Epoch 702/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 4.6136e-06 - val_loss: 6.9046e-06
Epoch 703/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 1.1738e-05 - val_loss: 8.1944e-06
Epoch 704/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 4.2993e-06 - val_loss: 4.3426e-06
Epoch 705/1000
3888/3888 [==============================] - 1s 161us/sample - loss: 4.0264e-06 - val_loss: 2.9858e-06
Epoch 706/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 5.5883e-06 - val_loss: 3.0930e-06
Epoch 707/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 5.6384e-06 - val_loss: 1.9893e-05
Epoch 708/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 1.2308e-05 - val_loss: 2.2723e-06
Epoch 709/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 2.4917e-06 - val_loss: 4.6097e-06
Epoch 710/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 6.5434e-06 - val_loss: 2.2377e-06
Epoch 711/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 8.5757e-06 - val_loss: 2.1981e-06
Epoch 712/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 5.5690e-06 - val_loss: 1.9117e-06
Epoch 713/1000
3888/3888 [==============================] - 1s 162us/sample - loss: 7.6084e-06 - val_loss: 3.1844e-06
Epoch 714/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 4.9737e-06 - val_loss: 2.4693e-06
Epoch 715/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 5.2299e-06 - val_loss: 1.7391e-05
Epoch 716/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 3.4171e-06 - val_loss: 1.0237e-05
Epoch 717/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 1.0039e-05 - val_loss: 8.3671e-05
Epoch 718/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 1.0087e-05 - val_loss: 2.2911e-06
Epoch 719/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 3.3686e-06 - val_loss: 2.0400e-06
Epoch 720/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 7.8005e-06 - val_loss: 2.4595e-06
Epoch 721/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 3.7221e-06 - val_loss: 2.6916e-06
Epoch 722/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 9.2297e-06 - val_loss: 5.7757e-06
Epoch 723/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 3.6983e-06 - val_loss: 2.5092e-06
Epoch 724/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 9.0859e-06 - val_loss: 1.3905e-05
Epoch 725/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 6.1067e-06 - val_loss: 2.8300e-06
Epoch 726/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 3.2672e-06 - val_loss: 3.4278e-06
Epoch 727/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 4.6965e-06 - val_loss: 3.1145e-06
Epoch 728/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 1.5177e-05 - val_loss: 2.2529e-06
Epoch 729/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 7.2252e-06 - val_loss: 2.1555e-06
Epoch 730/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 3.8634e-06 - val_loss: 1.8502e-05
Epoch 731/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 8.6109e-06 - val_loss: 1.6665e-06
Epoch 732/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 2.4749e-06 - val_loss: 3.4787e-06
Epoch 733/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 8.6011e-06 - val_loss: 7.2570e-06
Epoch 734/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 2.7702e-06 - val_loss: 5.3229e-06
Epoch 735/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 1.2463e-05 - val_loss: 2.3051e-06
Epoch 736/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 2.8293e-06 - val_loss: 1.5986e-06
Epoch 737/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 5.1986e-06 - val_loss: 8.7274e-06
Epoch 738/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 6.5860e-06 - val_loss: 2.3562e-06
Epoch 739/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 2.8723e-06 - val_loss: 3.6557e-06
Epoch 740/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 8.5597e-06 - val_loss: 3.2005e-06
Epoch 741/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 6.6909e-06 - val_loss: 1.3349e-05
Epoch 742/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 1.7086e-05 - val_loss: 2.2086e-06
Epoch 743/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 2.2284e-06 - val_loss: 5.4729e-06
Epoch 744/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 3.8830e-06 - val_loss: 3.4757e-06
Epoch 745/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 6.9488e-06 - val_loss: 4.6278e-06
Epoch 746/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 4.2556e-06 - val_loss: 5.0094e-06
Epoch 747/1000
3888/3888 [==============================] - 1s 162us/sample - loss: 6.9013e-06 - val_loss: 2.0116e-06
Epoch 748/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 2.1696e-05 - val_loss: 3.2718e-06
Epoch 749/1000
3888/3888 [==============================] - 1s 162us/sample - loss: 1.7895e-06 - val_loss: 1.7937e-06
Epoch 750/1000
3888/3888 [==============================] - 1s 162us/sample - loss: 1.6886e-06 - val_loss: 3.2168e-06
Epoch 751/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 3.0437e-06 - val_loss: 5.7697e-06
Epoch 752/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 6.8203e-06 - val_loss: 2.0313e-06
Epoch 753/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 4.9564e-06 - val_loss: 6.3141e-06
Epoch 754/1000
3888/3888 [==============================] - 1s 162us/sample - loss: 6.0875e-06 - val_loss: 2.8463e-05
Epoch 755/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 1.8737e-05 - val_loss: 2.9726e-06
Epoch 756/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 2.1037e-06 - val_loss: 4.8715e-06
Epoch 757/1000
3888/3888 [==============================] - 1s 161us/sample - loss: 2.2620e-06 - val_loss: 3.6488e-06
Epoch 758/1000
3888/3888 [==============================] - 1s 162us/sample - loss: 4.2694e-06 - val_loss: 3.5380e-06
Epoch 759/1000
3888/3888 [==============================] - 1s 161us/sample - loss: 1.0423e-05 - val_loss: 2.3417e-06
Epoch 760/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 7.2690e-06 - val_loss: 7.6126e-06
Epoch 761/1000
3888/3888 [==============================] - 1s 161us/sample - loss: 5.1283e-06 - val_loss: 2.3281e-06
Epoch 762/1000
3888/3888 [==============================] - 1s 162us/sample - loss: 3.7424e-06 - val_loss: 1.9560e-06
Epoch 763/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 5.0716e-06 - val_loss: 5.1792e-06
Epoch 764/1000
3888/3888 [==============================] - 1s 162us/sample - loss: 1.0411e-05 - val_loss: 3.2560e-06
Epoch 765/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 6.6007e-06 - val_loss: 1.2112e-05
Epoch 766/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 6.1528e-06 - val_loss: 2.1560e-06
Epoch 767/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 2.7834e-06 - val_loss: 2.7505e-06
Epoch 768/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 6.0295e-06 - val_loss: 3.5703e-06
Epoch 769/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 9.8851e-06 - val_loss: 4.5103e-06
Epoch 770/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 5.6137e-06 - val_loss: 5.0688e-05
Epoch 771/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 6.9347e-06 - val_loss: 1.8578e-06
Epoch 772/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 5.8994e-06 - val_loss: 3.4737e-06
Epoch 773/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 3.1411e-06 - val_loss: 2.3996e-06
Epoch 774/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 7.5358e-06 - val_loss: 9.5618e-06
Epoch 775/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 4.3155e-06 - val_loss: 5.0994e-06
Epoch 776/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 7.4584e-06 - val_loss: 2.0672e-06
Epoch 777/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 6.9611e-06 - val_loss: 4.6562e-06
Epoch 778/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 7.5168e-06 - val_loss: 1.4829e-06
Epoch 779/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 9.2897e-06 - val_loss: 4.8001e-06
Epoch 780/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 3.2998e-06 - val_loss: 4.3309e-06
Epoch 781/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 5.5847e-06 - val_loss: 8.4400e-06
Epoch 782/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 4.3959e-06 - val_loss: 5.8241e-05
Epoch 783/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 7.6059e-06 - val_loss: 2.2832e-06
Epoch 784/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 3.5673e-06 - val_loss: 2.8397e-06
Epoch 785/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 1.5470e-05 - val_loss: 2.6250e-06
Epoch 786/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 2.1511e-06 - val_loss: 3.4366e-06
Epoch 787/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 4.1752e-06 - val_loss: 2.7688e-06
Epoch 788/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 5.3467e-06 - val_loss: 6.1376e-06
Epoch 789/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 7.3430e-06 - val_loss: 1.8958e-06
Epoch 790/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 3.3303e-06 - val_loss: 5.4627e-06
Epoch 791/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 9.2902e-06 - val_loss: 7.9086e-06
Epoch 792/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 9.4993e-06 - val_loss: 4.4534e-06
Epoch 793/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 3.2254e-06 - val_loss: 8.9466e-06
Epoch 794/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 2.7849e-06 - val_loss: 6.6420e-06
Epoch 795/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 7.6654e-06 - val_loss: 3.2498e-06
Epoch 796/1000
3888/3888 [==============================] - 1s 162us/sample - loss: 7.6201e-06 - val_loss: 1.8296e-05
Epoch 797/1000
3888/3888 [==============================] - 1s 162us/sample - loss: 7.4328e-06 - val_loss: 6.3781e-06
Epoch 798/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 5.7137e-06 - val_loss: 2.0336e-06
Epoch 799/1000
3888/3888 [==============================] - 1s 162us/sample - loss: 3.0551e-06 - val_loss: 1.3844e-05
Epoch 800/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 1.0619e-05 - val_loss: 1.7140e-05
Epoch 801/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 5.1146e-06 - val_loss: 7.6266e-06
Epoch 802/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 4.6091e-06 - val_loss: 4.6041e-06
Epoch 803/1000
3888/3888 [==============================] - 1s 160us/sample - loss: 9.5688e-06 - val_loss: 2.4609e-06
Epoch 804/1000
3888/3888 [==============================] - 1s 160us/sample - loss: 3.0091e-06 - val_loss: 2.1043e-06
Epoch 805/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 6.2905e-06 - val_loss: 2.7834e-06
Epoch 806/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 6.3295e-06 - val_loss: 1.5077e-05
Epoch 807/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 4.7134e-06 - val_loss: 2.2854e-06
Epoch 808/1000
3888/3888 [==============================] - 1s 161us/sample - loss: 1.4392e-05 - val_loss: 3.7948e-06
Epoch 809/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 2.4486e-06 - val_loss: 9.5667e-06
Epoch 810/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 4.2050e-06 - val_loss: 9.7170e-06
Epoch 811/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 7.2433e-06 - val_loss: 2.9135e-06
Epoch 812/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 4.1999e-06 - val_loss: 3.7421e-06
Epoch 813/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 1.1655e-05 - val_loss: 2.4722e-06
Epoch 814/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 6.4282e-06 - val_loss: 5.6160e-06
Epoch 815/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 2.3381e-06 - val_loss: 3.8852e-06
Epoch 816/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 4.6563e-06 - val_loss: 9.2089e-06
Epoch 817/1000
3888/3888 [==============================] - 1s 162us/sample - loss: 7.7846e-06 - val_loss: 2.0207e-06
Epoch 818/1000
3888/3888 [==============================] - 1s 161us/sample - loss: 2.5294e-06 - val_loss: 3.9383e-06
Epoch 819/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 4.2620e-06 - val_loss: 2.9572e-06
Epoch 820/1000
3888/3888 [==============================] - 1s 161us/sample - loss: 1.7502e-05 - val_loss: 1.9467e-06
Epoch 821/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 2.1084e-06 - val_loss: 4.7428e-06
Epoch 822/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 3.5549e-06 - val_loss: 9.1445e-06
Epoch 823/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 6.5881e-06 - val_loss: 2.3637e-06
Epoch 824/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 3.9334e-06 - val_loss: 3.1392e-06
Epoch 825/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 9.5863e-06 - val_loss: 2.0007e-06
Epoch 826/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 1.2421e-05 - val_loss: 2.0864e-06
Epoch 827/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 2.1206e-06 - val_loss: 2.4137e-06
Epoch 828/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 3.5770e-06 - val_loss: 4.1399e-06
Epoch 829/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 1.0052e-05 - val_loss: 1.1299e-04
Epoch 830/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 6.7765e-06 - val_loss: 1.8016e-06
Epoch 831/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 2.0182e-06 - val_loss: 2.2805e-06
Epoch 832/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 3.0929e-06 - val_loss: 2.1839e-06
Epoch 833/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 6.7647e-06 - val_loss: 7.4611e-06
Epoch 834/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 5.8294e-06 - val_loss: 2.1110e-06
Epoch 835/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 8.6005e-06 - val_loss: 2.5510e-06
Epoch 836/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 2.1009e-06 - val_loss: 4.8268e-06
Epoch 837/1000
3888/3888 [==============================] - 1s 173us/sample - loss: 7.8965e-06 - val_loss: 6.7347e-06
Epoch 838/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 6.3969e-06 - val_loss: 2.3511e-06
Epoch 839/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 2.0199e-05 - val_loss: 2.6520e-06
Epoch 840/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 1.7535e-06 - val_loss: 2.9943e-06
Epoch 841/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 2.3540e-06 - val_loss: 2.8025e-06
Epoch 842/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 1.0953e-05 - val_loss: 1.4128e-06
Epoch 843/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 2.1547e-06 - val_loss: 2.5967e-06
Epoch 844/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 2.1284e-06 - val_loss: 4.5697e-06
Epoch 845/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 6.2030e-06 - val_loss: 2.9638e-06
Epoch 846/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 6.7587e-06 - val_loss: 2.0322e-06
Epoch 847/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 2.2278e-06 - val_loss: 5.1796e-06
Epoch 848/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 1.1164e-05 - val_loss: 1.9499e-06
Epoch 849/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 4.7049e-06 - val_loss: 2.8449e-06
Epoch 850/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 1.6122e-05 - val_loss: 3.3790e-05
Epoch 851/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 4.0325e-06 - val_loss: 1.4015e-06
Epoch 852/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 4.6529e-06 - val_loss: 1.4511e-06
Epoch 853/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 2.2267e-06 - val_loss: 3.1475e-06
Epoch 854/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 3.6639e-06 - val_loss: 3.1564e-05
Epoch 855/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 7.0384e-06 - val_loss: 3.1758e-06
Epoch 856/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 1.0654e-05 - val_loss: 2.9913e-06
Epoch 857/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 2.4661e-06 - val_loss: 1.3029e-06
Epoch 858/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 6.8052e-06 - val_loss: 7.6139e-06
Epoch 859/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 6.5722e-06 - val_loss: 2.8367e-05
Epoch 860/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 2.5544e-06 - val_loss: 1.2088e-05
Epoch 861/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 5.1299e-06 - val_loss: 2.9148e-06
Epoch 862/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 9.3464e-06 - val_loss: 3.2254e-05
Epoch 863/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 4.1837e-06 - val_loss: 1.4637e-06
Epoch 864/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 2.1099e-06 - val_loss: 2.0721e-06
Epoch 865/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 7.5095e-06 - val_loss: 3.8327e-06
Epoch 866/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 3.6482e-06 - val_loss: 7.2715e-06
Epoch 867/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 7.6010e-06 - val_loss: 5.7788e-05
Epoch 868/1000
3888/3888 [==============================] - 1s 162us/sample - loss: 6.3916e-06 - val_loss: 1.0005e-05
Epoch 869/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 6.2830e-06 - val_loss: 1.6308e-06
Epoch 870/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 5.3658e-06 - val_loss: 2.4649e-06
Epoch 871/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 8.7231e-06 - val_loss: 7.7415e-06
Epoch 872/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 3.8396e-06 - val_loss: 8.2775e-06
Epoch 873/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 4.1010e-06 - val_loss: 8.1543e-06
Epoch 874/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 4.6043e-06 - val_loss: 1.7351e-06
Epoch 875/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 8.4081e-06 - val_loss: 3.2645e-06
Epoch 876/1000
3888/3888 [==============================] - 1s 162us/sample - loss: 4.0041e-06 - val_loss: 2.1227e-06
Epoch 877/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 1.2496e-05 - val_loss: 3.9363e-06
Epoch 878/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 2.1125e-06 - val_loss: 1.4032e-05
Epoch 879/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 3.7346e-06 - val_loss: 4.6809e-06
Epoch 880/1000
3888/3888 [==============================] - 1s 161us/sample - loss: 6.8622e-06 - val_loss: 4.6001e-06
Epoch 881/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 5.8242e-06 - val_loss: 2.2288e-06
Epoch 882/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 3.3127e-06 - val_loss: 1.9237e-06
Epoch 883/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 4.0922e-06 - val_loss: 1.7614e-06
Epoch 884/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 5.8579e-06 - val_loss: 4.6058e-06
Epoch 885/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 9.8404e-06 - val_loss: 1.3702e-06
Epoch 886/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 1.9641e-06 - val_loss: 3.1501e-06
Epoch 887/1000
3888/3888 [==============================] - 1s 162us/sample - loss: 4.7577e-06 - val_loss: 2.7389e-06
Epoch 888/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 2.4902e-05 - val_loss: 7.0653e-06
Epoch 889/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 2.0851e-06 - val_loss: 1.7295e-06
Epoch 890/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 1.7044e-06 - val_loss: 1.9539e-06
Epoch 891/1000
3888/3888 [==============================] - 1s 158us/sample - loss: 3.0548e-06 - val_loss: 1.7713e-06
Epoch 892/1000
3888/3888 [==============================] - 1s 160us/sample - loss: 2.5777e-06 - val_loss: 3.0858e-06
Epoch 893/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 9.8964e-06 - val_loss: 4.3673e-06
Epoch 894/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 2.4679e-06 - val_loss: 1.4053e-06
Epoch 895/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 5.3370e-06 - val_loss: 2.3683e-06
Epoch 896/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 6.9820e-06 - val_loss: 2.4671e-06
Epoch 897/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 3.2697e-06 - val_loss: 3.8837e-06
Epoch 898/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 7.1294e-06 - val_loss: 1.5857e-05
Epoch 899/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 4.7637e-06 - val_loss: 2.5314e-05
Epoch 900/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 4.0619e-06 - val_loss: 3.0389e-06
Epoch 901/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 5.5887e-06 - val_loss: 5.8972e-06
Epoch 902/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 7.2578e-06 - val_loss: 1.8121e-06
Epoch 903/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 2.5340e-05 - val_loss: 1.9057e-06
Epoch 904/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 1.6592e-06 - val_loss: 1.3450e-06
Epoch 905/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 1.3561e-06 - val_loss: 1.6019e-06
Epoch 906/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 2.5925e-06 - val_loss: 4.7183e-06
Epoch 907/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 4.8295e-06 - val_loss: 1.0839e-05
Epoch 908/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 2.7352e-06 - val_loss: 3.6938e-06
Epoch 909/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 3.7493e-06 - val_loss: 5.1690e-06
Epoch 910/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 5.9894e-06 - val_loss: 2.5425e-05
Epoch 911/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 1.4853e-05 - val_loss: 4.6569e-06
Epoch 912/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 2.5471e-06 - val_loss: 3.3032e-06
Epoch 913/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 3.1720e-06 - val_loss: 3.1734e-06
Epoch 914/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 3.1823e-06 - val_loss: 4.0763e-06
Epoch 915/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 1.6357e-05 - val_loss: 1.5139e-06
Epoch 916/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 1.4681e-06 - val_loss: 1.5220e-06
Epoch 917/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 1.9307e-06 - val_loss: 1.3779e-06
Epoch 918/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 1.5191e-05 - val_loss: 3.8788e-06
Epoch 919/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 1.7677e-06 - val_loss: 4.1557e-06
Epoch 920/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 3.5336e-06 - val_loss: 1.4581e-05
Epoch 921/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 2.7531e-06 - val_loss: 8.8573e-06
Epoch 922/1000
3888/3888 [==============================] - 1s 177us/sample - loss: 3.7063e-06 - val_loss: 5.4229e-06
Epoch 923/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 1.0532e-05 - val_loss: 1.7654e-06
Epoch 924/1000
3888/3888 [==============================] - 1s 174us/sample - loss: 5.5829e-06 - val_loss: 1.9428e-06
Epoch 925/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 2.7866e-06 - val_loss: 9.8100e-06
Epoch 926/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 1.0816e-05 - val_loss: 1.7568e-06
Epoch 927/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 2.0562e-06 - val_loss: 4.1915e-06
Epoch 928/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 5.6562e-06 - val_loss: 2.1544e-06
Epoch 929/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 2.9427e-06 - val_loss: 4.1118e-06
Epoch 930/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 5.1345e-06 - val_loss: 3.0541e-06
Epoch 931/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 5.2787e-06 - val_loss: 1.5460e-05
Epoch 932/1000
3888/3888 [==============================] - 1s 171us/sample - loss: 1.6622e-05 - val_loss: 9.7273e-06
Epoch 933/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 2.4480e-06 - val_loss: 1.6005e-06
Epoch 934/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 2.3727e-06 - val_loss: 1.7770e-06
Epoch 935/1000
3888/3888 [==============================] - 1s 164us/sample - loss: 3.6466e-06 - val_loss: 2.2452e-06
Epoch 936/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 5.9288e-06 - val_loss: 1.4226e-06
Epoch 937/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 4.4455e-06 - val_loss: 2.3434e-06
Epoch 938/1000
3888/3888 [==============================] - 1s 167us/sample - loss: 7.6965e-06 - val_loss: 3.0733e-06
Epoch 939/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 3.0304e-06 - val_loss: 5.1687e-06
Epoch 940/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 6.8483e-06 - val_loss: 1.7941e-06
Epoch 941/1000
3888/3888 [==============================] - 1s 169us/sample - loss: 5.6291e-06 - val_loss: 4.0073e-06
Epoch 942/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 1.1260e-05 - val_loss: 6.6182e-06
Epoch 943/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 2.4625e-06 - val_loss: 1.5301e-06
Epoch 944/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 2.5897e-06 - val_loss: 2.4575e-06
Epoch 945/1000
3888/3888 [==============================] - 1s 162us/sample - loss: 9.6700e-06 - val_loss: 3.8631e-06
Epoch 946/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 2.5730e-06 - val_loss: 5.1494e-06
Epoch 947/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 5.1154e-06 - val_loss: 5.0446e-06
Epoch 948/1000
3888/3888 [==============================] - 1s 168us/sample - loss: 7.4122e-06 - val_loss: 2.4344e-05
Epoch 949/1000
3888/3888 [==============================] - 1s 163us/sample - loss: 3.0407e-06 - val_loss: 1.8600e-06
Epoch 950/1000
3888/3888 [==============================] - 1s 165us/sample - loss: 4.1492e-06 - val_loss: 1.9007e-06
Epoch 951/1000
3888/3888 [==============================] - 1s 172us/sample - loss: 5.8114e-06 - val_loss: 1.1793e-05
Epoch 952/1000
3888/3888 [==============================] - 1s 170us/sample - loss: 3.5881e-06 - val_loss: 3.4288e-06
Epoch 953/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 5.6412e-06 - val_loss: 2.0950e-06
Epoch 954/1000
3888/3888 [==============================] - 1s 162us/sample - loss: 7.5404e-06 - val_loss: 2.9750e-06
Epoch 955/1000
3888/3888 [==============================] - 1s 162us/sample - loss: 6.3202e-06 - val_loss: 1.8026e-05
Epoch 956/1000
3888/3888 [==============================] - 1s 166us/sample - loss: 2.5637e-06 - val_loss: 8.9821e-06
Epoch 957/1000
3616/3888 [==========================>...] - ETA: 0s - loss: 5.6745e-06Restoring model weights from the end of the best epoch.
3888/3888 [==============================] - 1s 166us/sample - loss: 6.0992e-06 - val_loss: 2.3747e-05
Epoch 00957: early stopping
In [46]:
print(history.history.keys())
print('best value: ', autoencoder.evaluate(X_train_1D_norm, X_train_1D_norm, verbose=0))


pd.DataFrame(history.history).plot(figsize=(8, 5), logy=True)
plt.grid()
dict_keys(['loss', 'val_loss'])
best value:  1.3029401012924306e-06
In [47]:
X_reconstructions = autoencoder.predict(X_train_1D_norm)
X_reconstructions = stdscaler.inverse_transform(X_reconstructions)
In [48]:
calculateerror(X_train_1D.reshape(len(times),len(groups),nl,nc), 
               X_reconstructions.reshape(len(times),len(groups),nl,nc), 
               groups,
               print_step=0)
max_abs_error:  11.947998046875
mean_abs_error:  0.018922157238844824
/home/viluiz/anaconda3/envs/py3ml/lib/python3.7/site-packages/ipykernel_launcher.py:3: RuntimeWarning: divide by zero encountered in true_divide
  This is separate from the ipykernel package so we can avoid doing imports until
/home/viluiz/anaconda3/envs/py3ml/lib/python3.7/site-packages/ipykernel_launcher.py:3: RuntimeWarning: invalid value encountered in true_divide
  This is separate from the ipykernel package so we can avoid doing imports until
In [49]:
fig, ax = plt.subplots(2,4, figsize=[20,10])
for i, group in enumerate(groups):
    im = ax.flatten()[i].imshow(X_reconstructions.reshape(len(times),len(groups),nl,nc)[100,i,:,:])
    fig.colorbar(im, ax=ax.flatten()[i])
    ax.flatten()[i].set_title(group)
In [50]:
fig, ax = plt.subplots(2,4, figsize=[20,10])
for i, group in enumerate(groups):
    ax.flatten()[i].plot(times, X_train_1D[:,i*nl*nc+4])
    ax.flatten()[i].plot(times, X_reconstructions[:,i*nl*nc+4],'--')
    ax.flatten()[i].set_title(group)

PCA Non-linear autoencoder

In [51]:
from sklearn.decomposition import PCA

pca = PCA(n_components=115)
X_train_pca = pca.fit_transform(X_train_1D)
In [52]:
for i, s in enumerate(pca.singular_values_):
    print(i,s)
0 80968.84766232983
1 18736.416256363147
2 13673.705906493908
3 5582.676911420286
4 3850.103044758365
5 1800.019343080367
6 1528.5522031979297
7 1280.5509296354314
8 732.9460285895258
9 435.26801371011277
10 297.1644606962294
11 181.60261273092226
12 87.67587246299661
13 64.75204695667632
14 56.14576255903517
15 22.805565355182708
16 14.680854372754887
17 9.433904365360851
18 8.086013100546879
19 6.691068654905932
20 3.580681950463192
21 3.20088686807251
22 2.9496893087417306
23 1.3593700244681008
24 1.303570351076243
25 0.9841955380229419
26 0.8745552427561293
27 0.6751953659028838
28 0.45335331999123346
29 0.3682248610588608
30 0.34029883857464993
31 0.30108410171505134
32 0.2747720601623404
33 0.2580691302875648
34 0.25155019818685037
35 0.23908841104002337
36 0.231200546275467
37 0.2264119861037694
38 0.2231818070561315
39 0.22132948289422547
40 0.21754946426500482
41 0.1964041659361343
42 0.1679801080275365
43 0.15181483405154386
44 0.14977231392571658
45 0.1473383360201773
46 0.14617716679239093
47 0.1453544112672034
48 0.14453011030302515
49 0.1439177002926129
50 0.14160316373151513
51 0.13906581761656092
52 0.13780621050453318
53 0.13740573893534178
54 0.13666581093000651
55 0.13493310745883458
56 0.1332568496983295
57 0.13291836640430776
58 0.1307904036435818
59 0.13070402948183749
60 0.1292305310358126
61 0.1279535385999949
62 0.12723542380683203
63 0.1268304817673247
64 0.12614436133224236
65 0.12489907480215162
66 0.12360679751365647
67 0.1219607164200381
68 0.12142235784823262
69 0.11993204400816027
70 0.11937139251378552
71 0.11747907576501383
72 0.11617850864659925
73 0.1155368010649835
74 0.1144785725500825
75 0.1128507232536807
76 0.11206714316404931
77 0.11071054748169312
78 0.10711683369304364
79 0.10560161003677218
80 0.0007051765903159103
81 0.0007049224859099373
82 0.00011454367898059194
83 9.983968383730085e-05
84 9.968887287803789e-05
85 7.40362947509492e-05
86 7.08901089558992e-05
87 7.08383287927755e-05
88 7.07760356261245e-05
89 7.074115206083078e-05
90 7.046377936715971e-05
91 7.03625872480673e-05
92 7.00733821003764e-05
93 4.467147195296282e-05
94 2.8083779709223102e-05
95 7.047996459632438e-06
96 5.655737100821686e-06
97 5.947717937151221e-07
98 4.883996219172943e-07
99 2.3762879983255165e-07
100 2.1838297456611626e-07
101 1.9162186180281583e-07
102 1.644840238306863e-07
103 1.4385138440225086e-07
104 1.352797419456935e-07
105 1.232880930271776e-07
106 1.1190936811411953e-07
107 1.025810141391199e-07
108 9.480668974466976e-08
109 8.381246249793427e-08
110 7.394636004087645e-08
111 4.5573931030267163e-08
112 3.9864671094623444e-08
113 1.501245627371161e-08
114 8.090413105687467e-12
In [53]:
np.random.seed(42)
tf.random.set_seed(42)

# Need to have validation loss
early_stopping = keras.callbacks.EarlyStopping(monitor='val_loss',
                                               min_delta=0.0,
                                               patience=100,
                                               verbose=2,
                                               restore_best_weights=True)

encoder = keras.models.Sequential([keras.layers.Dense(100, input_shape=[115], activation="elu"),
                                   keras.layers.Dense(50, activation="elu"),
                                   keras.layers.Dense(15)])
decoder = keras.models.Sequential([keras.layers.Dense(50, input_shape=[15], activation="elu"),
                                   keras.layers.Dense(100, activation="elu"),
                                   keras.layers.Dense(115),
                                  ])
autoencoder = keras.models.Sequential([encoder, decoder])

autoencoder.compile(loss="mse", 
                    optimizer=keras.optimizers.Nadam(lr=0.0003, beta_1=0.9, beta_2=0.999)
                    )
encoder.summary()
decoder.summary()
Model: "sequential_9"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_12 (Dense)             (None, 100)               11600     
_________________________________________________________________
dense_13 (Dense)             (None, 50)                5050      
_________________________________________________________________
dense_14 (Dense)             (None, 15)                765       
=================================================================
Total params: 17,415
Trainable params: 17,415
Non-trainable params: 0
_________________________________________________________________
Model: "sequential_10"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_15 (Dense)             (None, 50)                800       
_________________________________________________________________
dense_16 (Dense)             (None, 100)               5100      
_________________________________________________________________
dense_17 (Dense)             (None, 115)               11615     
=================================================================
Total params: 17,515
Trainable params: 17,515
Non-trainable params: 0
_________________________________________________________________
In [54]:
history = autoencoder.fit(X_train_pca, 
                          X_train_pca, 
                          epochs=1000,
                          validation_data=(X_train_pca, X_train_pca),
                          callbacks=[early_stopping])
Train on 3888 samples, validate on 3888 samples
Epoch 1/1000
3888/3888 [==============================] - 1s 350us/sample - loss: 8216.8399 - val_loss: 1035.5376
Epoch 2/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 734.8472 - val_loss: 478.6631
Epoch 3/1000
3888/3888 [==============================] - 0s 108us/sample - loss: 287.6702 - val_loss: 164.8730
Epoch 4/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 114.3151 - val_loss: 85.6140
Epoch 5/1000
3888/3888 [==============================] - 0s 108us/sample - loss: 74.4078 - val_loss: 64.5016
Epoch 6/1000
3888/3888 [==============================] - 0s 108us/sample - loss: 56.8069 - val_loss: 48.2749
Epoch 7/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 39.9972 - val_loss: 31.4465
Epoch 8/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 27.3115 - val_loss: 26.8397
Epoch 9/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 24.1810 - val_loss: 20.3460
Epoch 10/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 20.6134 - val_loss: 20.0034
Epoch 11/1000
3888/3888 [==============================] - 0s 106us/sample - loss: 18.6640 - val_loss: 16.0814
Epoch 12/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 17.6694 - val_loss: 15.7350
Epoch 13/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 17.3316 - val_loss: 14.4572
Epoch 14/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 15.0136 - val_loss: 19.0059
Epoch 15/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 15.0771 - val_loss: 22.0533
Epoch 16/1000
3888/3888 [==============================] - 0s 106us/sample - loss: 18.5594 - val_loss: 12.7963
Epoch 17/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 12.9352 - val_loss: 18.6959
Epoch 18/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 13.1217 - val_loss: 12.4854
Epoch 19/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 12.0637 - val_loss: 12.3078
Epoch 20/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 12.3319 - val_loss: 11.1737
Epoch 21/1000
3888/3888 [==============================] - 0s 106us/sample - loss: 10.7176 - val_loss: 11.1456
Epoch 22/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 11.7356 - val_loss: 10.3615
Epoch 23/1000
3888/3888 [==============================] - 0s 106us/sample - loss: 11.0722 - val_loss: 18.2247
Epoch 24/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 9.6776 - val_loss: 8.0109
Epoch 25/1000
3888/3888 [==============================] - 0s 104us/sample - loss: 10.0381 - val_loss: 9.3924
Epoch 26/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 8.5657 - val_loss: 7.8695
Epoch 27/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 8.5826 - val_loss: 7.0682
Epoch 28/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 6.6980 - val_loss: 10.2599
Epoch 29/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 7.9348 - val_loss: 6.7623
Epoch 30/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 6.3146 - val_loss: 11.6546
Epoch 31/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 6.6887 - val_loss: 4.7955
Epoch 32/1000
3888/3888 [==============================] - 0s 108us/sample - loss: 6.2727 - val_loss: 6.3317
Epoch 33/1000
3888/3888 [==============================] - 0s 108us/sample - loss: 5.0163 - val_loss: 4.6039
Epoch 34/1000
3888/3888 [==============================] - 0s 108us/sample - loss: 5.0681 - val_loss: 4.4893
Epoch 35/1000
3888/3888 [==============================] - 0s 108us/sample - loss: 6.2797 - val_loss: 5.4832
Epoch 36/1000
3888/3888 [==============================] - 0s 108us/sample - loss: 4.2529 - val_loss: 7.7428
Epoch 37/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 5.7423 - val_loss: 3.6946
Epoch 38/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 3.7593 - val_loss: 4.4625
Epoch 39/1000
3888/3888 [==============================] - 0s 108us/sample - loss: 6.1392 - val_loss: 3.2314
Epoch 40/1000
3888/3888 [==============================] - 0s 108us/sample - loss: 3.1810 - val_loss: 5.2926
Epoch 41/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 3.9926 - val_loss: 3.0844
Epoch 42/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 4.7274 - val_loss: 3.1449
Epoch 43/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 3.1673 - val_loss: 3.3040
Epoch 44/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 3.6463 - val_loss: 6.1856
Epoch 45/1000
3888/3888 [==============================] - 0s 108us/sample - loss: 3.1306 - val_loss: 2.8394
Epoch 46/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 3.6238 - val_loss: 3.8606
Epoch 47/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 3.5124 - val_loss: 2.4359
Epoch 48/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 3.1312 - val_loss: 3.6617
Epoch 49/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 6.2187 - val_loss: 2.4693
Epoch 50/1000
3888/3888 [==============================] - 0s 108us/sample - loss: 2.3745 - val_loss: 2.2090
Epoch 51/1000
3888/3888 [==============================] - 0s 108us/sample - loss: 2.5612 - val_loss: 2.2610
Epoch 52/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 3.2424 - val_loss: 2.4355
Epoch 53/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 3.2387 - val_loss: 3.8992
Epoch 54/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 2.1877 - val_loss: 2.1899
Epoch 55/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 3.2998 - val_loss: 2.1290
Epoch 56/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 2.2326 - val_loss: 3.1886
Epoch 57/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 2.5787 - val_loss: 1.8565
Epoch 58/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 2.1722 - val_loss: 3.6203
Epoch 59/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 2.7243 - val_loss: 2.6876
Epoch 60/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 2.7190 - val_loss: 3.9519
Epoch 61/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 2.0679 - val_loss: 2.4134
Epoch 62/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 2.8736 - val_loss: 2.9162
Epoch 63/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 2.3045 - val_loss: 1.5566
Epoch 64/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 1.8647 - val_loss: 1.8459
Epoch 65/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 1.8960 - val_loss: 1.6027
Epoch 66/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 2.2757 - val_loss: 2.0049
Epoch 67/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 2.5586 - val_loss: 1.8275
Epoch 68/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 1.5149 - val_loss: 3.1557
Epoch 69/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 1.9558 - val_loss: 5.4694
Epoch 70/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 2.0542 - val_loss: 1.3017
Epoch 71/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 2.0808 - val_loss: 1.3416
Epoch 72/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 1.7672 - val_loss: 6.4220
Epoch 73/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 2.0566 - val_loss: 1.5075
Epoch 74/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 1.3479 - val_loss: 1.2734
Epoch 75/1000
3888/3888 [==============================] - 0s 108us/sample - loss: 2.8215 - val_loss: 1.1642
Epoch 76/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 1.2874 - val_loss: 1.6600
Epoch 77/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 1.6195 - val_loss: 1.8074
Epoch 78/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 1.4400 - val_loss: 1.1657
Epoch 79/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 1.7366 - val_loss: 1.2417
Epoch 80/1000
3888/3888 [==============================] - 0s 108us/sample - loss: 1.6294 - val_loss: 1.1398
Epoch 81/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 1.3111 - val_loss: 1.8351
Epoch 82/1000
3888/3888 [==============================] - 0s 108us/sample - loss: 1.6109 - val_loss: 1.2803
Epoch 83/1000
3888/3888 [==============================] - 0s 108us/sample - loss: 1.5509 - val_loss: 1.2131
Epoch 84/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 1.5350 - val_loss: 1.2273
Epoch 85/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 1.8093 - val_loss: 1.1503
Epoch 86/1000
3888/3888 [==============================] - 0s 108us/sample - loss: 1.4707 - val_loss: 0.8966
Epoch 87/1000
3888/3888 [==============================] - 0s 108us/sample - loss: 1.8914 - val_loss: 1.4310
Epoch 88/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 1.0821 - val_loss: 1.0744
Epoch 89/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 1.5161 - val_loss: 4.8746
Epoch 90/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 1.4543 - val_loss: 1.4334
Epoch 91/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 1.4222 - val_loss: 1.4939
Epoch 92/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 1.1731 - val_loss: 1.4988
Epoch 93/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 1.1756 - val_loss: 0.9266
Epoch 94/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 1.4469 - val_loss: 1.0253
Epoch 95/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 1.8176 - val_loss: 0.8664
Epoch 96/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 0.9648 - val_loss: 1.0377
Epoch 97/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 1.1351 - val_loss: 3.4181
Epoch 98/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 1.3361 - val_loss: 1.0682
Epoch 99/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 1.4501 - val_loss: 1.7614
Epoch 100/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 1.0410 - val_loss: 1.0233
Epoch 101/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 1.0815 - val_loss: 1.1423
Epoch 102/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 1.3637 - val_loss: 2.3318
Epoch 103/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 1.1442 - val_loss: 1.0397
Epoch 104/1000
3888/3888 [==============================] - 0s 108us/sample - loss: 1.2897 - val_loss: 0.8084
Epoch 105/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 1.0296 - val_loss: 0.8900
Epoch 106/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 1.6222 - val_loss: 1.1878
Epoch 107/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 1.1859 - val_loss: 1.0418
Epoch 108/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.8976 - val_loss: 0.9111
Epoch 109/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 1.8155 - val_loss: 0.8472
Epoch 110/1000
3888/3888 [==============================] - 0s 106us/sample - loss: 0.7887 - val_loss: 0.8219
Epoch 111/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.9838 - val_loss: 1.0034
Epoch 112/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.9982 - val_loss: 1.6208
Epoch 113/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.9630 - val_loss: 1.6363
Epoch 114/1000
3888/3888 [==============================] - 0s 118us/sample - loss: 1.1791 - val_loss: 0.9612
Epoch 115/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.9612 - val_loss: 2.0543
Epoch 116/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.9296 - val_loss: 0.9522
Epoch 117/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 1.3498 - val_loss: 1.1120
Epoch 118/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 1.0312 - val_loss: 0.7799
Epoch 119/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 0.9143 - val_loss: 0.8475
Epoch 120/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.9657 - val_loss: 0.8492
Epoch 121/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 1.0142 - val_loss: 2.9638
Epoch 122/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 1.0105 - val_loss: 1.1665
Epoch 123/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 1.2598 - val_loss: 0.6654
Epoch 124/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 1.3521 - val_loss: 4.6589
Epoch 125/1000
3888/3888 [==============================] - 0s 108us/sample - loss: 0.7538 - val_loss: 0.6329
Epoch 126/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 0.6902 - val_loss: 1.1616
Epoch 127/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 0.9577 - val_loss: 1.2226
Epoch 128/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 0.8392 - val_loss: 1.5217
Epoch 129/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 1.0894 - val_loss: 0.7025
Epoch 130/1000
3888/3888 [==============================] - 0s 105us/sample - loss: 0.7085 - val_loss: 3.8074
Epoch 131/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 1.0549 - val_loss: 1.5405
Epoch 132/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.9562 - val_loss: 0.6383
Epoch 133/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 0.6945 - val_loss: 1.1428
Epoch 134/1000
3888/3888 [==============================] - 0s 108us/sample - loss: 0.9887 - val_loss: 0.9811
Epoch 135/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 0.8502 - val_loss: 1.8200
Epoch 136/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 1.1426 - val_loss: 0.5464
Epoch 137/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.7358 - val_loss: 2.9150
Epoch 138/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.8490 - val_loss: 0.5434
Epoch 139/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.6872 - val_loss: 0.6051
Epoch 140/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.9126 - val_loss: 0.8126
Epoch 141/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.7404 - val_loss: 0.5384
Epoch 142/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 1.1845 - val_loss: 0.7142
Epoch 143/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.6414 - val_loss: 0.6885
Epoch 144/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.7735 - val_loss: 0.5251
Epoch 145/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.7328 - val_loss: 0.6404
Epoch 146/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.9080 - val_loss: 0.9105
Epoch 147/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.7505 - val_loss: 0.6369
Epoch 148/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.7557 - val_loss: 0.8681
Epoch 149/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.6928 - val_loss: 1.3653
Epoch 150/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.6620 - val_loss: 2.3189
Epoch 151/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 1.0340 - val_loss: 0.9976
Epoch 152/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.9673 - val_loss: 0.4628
Epoch 153/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.6260 - val_loss: 0.5743
Epoch 154/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.6380 - val_loss: 0.5684
Epoch 155/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.6946 - val_loss: 1.1423
Epoch 156/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.7326 - val_loss: 0.4652
Epoch 157/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 1.0057 - val_loss: 6.7324
Epoch 158/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 0.6059 - val_loss: 0.6790
Epoch 159/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.8157 - val_loss: 0.6086
Epoch 160/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.5720 - val_loss: 0.7312
Epoch 161/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 1.0858 - val_loss: 0.7631
Epoch 162/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.5493 - val_loss: 0.5134
Epoch 163/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.5508 - val_loss: 0.4698
Epoch 164/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.5643 - val_loss: 1.5089
Epoch 165/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.6657 - val_loss: 0.7396
Epoch 166/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 1.0131 - val_loss: 0.7039
Epoch 167/1000
3888/3888 [==============================] - 0s 118us/sample - loss: 0.5875 - val_loss: 0.8609
Epoch 168/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.6684 - val_loss: 0.4834
Epoch 169/1000
3888/3888 [==============================] - 0s 118us/sample - loss: 0.6034 - val_loss: 0.4738
Epoch 170/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.5876 - val_loss: 0.5982
Epoch 171/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.6162 - val_loss: 0.4592
Epoch 172/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.9735 - val_loss: 3.9072
Epoch 173/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.5683 - val_loss: 0.7324
Epoch 174/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.4736 - val_loss: 0.6436
Epoch 175/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.7635 - val_loss: 0.5221
Epoch 176/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.5022 - val_loss: 0.5284
Epoch 177/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.9081 - val_loss: 0.3940
Epoch 178/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.5417 - val_loss: 0.6338
Epoch 179/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.6509 - val_loss: 0.5283
Epoch 180/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.5980 - val_loss: 0.4690
Epoch 181/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 0.4802 - val_loss: 1.9992
Epoch 182/1000
3888/3888 [==============================] - 0s 108us/sample - loss: 0.6410 - val_loss: 0.4436
Epoch 183/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.8637 - val_loss: 4.1249
Epoch 184/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.5879 - val_loss: 0.3693
Epoch 185/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 0.4946 - val_loss: 0.4360
Epoch 186/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.4871 - val_loss: 0.3611
Epoch 187/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.8429 - val_loss: 0.7500
Epoch 188/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.4741 - val_loss: 0.4966
Epoch 189/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.5152 - val_loss: 0.4412
Epoch 190/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.5302 - val_loss: 0.5422
Epoch 191/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.6159 - val_loss: 0.7860
Epoch 192/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.9343 - val_loss: 0.3714
Epoch 193/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.3797 - val_loss: 0.4119
Epoch 194/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.9614 - val_loss: 1.1046
Epoch 195/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.4618 - val_loss: 0.3230
Epoch 196/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 0.3828 - val_loss: 0.7817
Epoch 197/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.6175 - val_loss: 0.3456
Epoch 198/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.3847 - val_loss: 0.3844
Epoch 199/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.5813 - val_loss: 0.3922
Epoch 200/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.6021 - val_loss: 0.3374
Epoch 201/1000
3888/3888 [==============================] - 0s 108us/sample - loss: 0.4812 - val_loss: 0.4325
Epoch 202/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.6186 - val_loss: 0.3100
Epoch 203/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 0.5373 - val_loss: 0.3897
Epoch 204/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.6631 - val_loss: 0.3726
Epoch 205/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.3617 - val_loss: 1.1166
Epoch 206/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.5764 - val_loss: 0.3329
Epoch 207/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.4529 - val_loss: 0.4618
Epoch 208/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.5060 - val_loss: 1.1717
Epoch 209/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.6154 - val_loss: 0.3748
Epoch 210/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.5711 - val_loss: 0.3233
Epoch 211/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.4372 - val_loss: 1.0693
Epoch 212/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 0.5397 - val_loss: 0.4055
Epoch 213/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.5550 - val_loss: 0.4316
Epoch 214/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.4883 - val_loss: 1.7524
Epoch 215/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.5401 - val_loss: 0.2791
Epoch 216/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.3796 - val_loss: 0.3850
Epoch 217/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.5851 - val_loss: 0.7781
Epoch 218/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.4598 - val_loss: 0.3279
Epoch 219/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.4044 - val_loss: 0.8433
Epoch 220/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.5715 - val_loss: 0.5616
Epoch 221/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.5648 - val_loss: 0.3714
Epoch 222/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.5455 - val_loss: 0.4319
Epoch 223/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.3983 - val_loss: 0.5357
Epoch 224/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.6957 - val_loss: 0.2656
Epoch 225/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.3856 - val_loss: 0.3817
Epoch 226/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 0.4755 - val_loss: 0.4138
Epoch 227/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.4430 - val_loss: 0.2712
Epoch 228/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.4744 - val_loss: 0.4675
Epoch 229/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.4947 - val_loss: 0.2937
Epoch 230/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.4393 - val_loss: 0.2935
Epoch 231/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.7524 - val_loss: 1.0370
Epoch 232/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.3340 - val_loss: 0.8246
Epoch 233/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.5298 - val_loss: 0.4243
Epoch 234/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.3120 - val_loss: 0.2703
Epoch 235/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.4062 - val_loss: 0.6819
Epoch 236/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.5868 - val_loss: 0.4592
Epoch 237/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.4618 - val_loss: 0.5651
Epoch 238/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 0.4403 - val_loss: 0.3998
Epoch 239/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.4485 - val_loss: 0.2443
Epoch 240/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 0.5178 - val_loss: 0.3245
Epoch 241/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.3858 - val_loss: 0.2397
Epoch 242/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.5087 - val_loss: 0.4250
Epoch 243/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 0.3187 - val_loss: 0.6091
Epoch 244/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.4811 - val_loss: 0.3518
Epoch 245/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.4983 - val_loss: 0.3290
Epoch 246/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.4482 - val_loss: 13.6777
Epoch 247/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.6413 - val_loss: 0.2412
Epoch 248/1000
3888/3888 [==============================] - 0s 108us/sample - loss: 0.4357 - val_loss: 0.7732
Epoch 249/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 0.3026 - val_loss: 0.2616
Epoch 250/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.5362 - val_loss: 0.3769
Epoch 251/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.2905 - val_loss: 0.2573
Epoch 252/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.3270 - val_loss: 0.3514
Epoch 253/1000
3888/3888 [==============================] - 0s 108us/sample - loss: 0.4122 - val_loss: 0.2422
Epoch 254/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.4569 - val_loss: 0.3905
Epoch 255/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.4234 - val_loss: 0.8740
Epoch 256/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.3737 - val_loss: 0.3015
Epoch 257/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.4645 - val_loss: 0.2305
Epoch 258/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.4733 - val_loss: 0.2199
Epoch 259/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.5157 - val_loss: 0.3957
Epoch 260/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.3820 - val_loss: 0.2170
Epoch 261/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 0.4262 - val_loss: 0.6148
Epoch 262/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.3734 - val_loss: 0.5266
Epoch 263/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.3270 - val_loss: 0.2189
Epoch 264/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.3403 - val_loss: 0.2379
Epoch 265/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.4678 - val_loss: 0.3071
Epoch 266/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.5544 - val_loss: 0.3708
Epoch 267/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.3362 - val_loss: 0.4895
Epoch 268/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.4220 - val_loss: 0.3320
Epoch 269/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.3395 - val_loss: 3.4882
Epoch 270/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.7336 - val_loss: 0.1818
Epoch 271/1000
3888/3888 [==============================] - 0s 108us/sample - loss: 0.2746 - val_loss: 0.2618
Epoch 272/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.3936 - val_loss: 0.3034
Epoch 273/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.4184 - val_loss: 0.2212
Epoch 274/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.3998 - val_loss: 0.4902
Epoch 275/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.3726 - val_loss: 0.4604
Epoch 276/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.4985 - val_loss: 0.2128
Epoch 277/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.2766 - val_loss: 0.2204
Epoch 278/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.3875 - val_loss: 0.3277
Epoch 279/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.3580 - val_loss: 0.2699
Epoch 280/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.4392 - val_loss: 0.1791
Epoch 281/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.3801 - val_loss: 0.1798
Epoch 282/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.5200 - val_loss: 0.3491
Epoch 283/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.4499 - val_loss: 0.1996
Epoch 284/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.2098 - val_loss: 0.3724
Epoch 285/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.2777 - val_loss: 0.2067
Epoch 286/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.5630 - val_loss: 0.4450
Epoch 287/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.3493 - val_loss: 1.4603
Epoch 288/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.3166 - val_loss: 1.2091
Epoch 289/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.3779 - val_loss: 0.2556
Epoch 290/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.4501 - val_loss: 0.2847
Epoch 291/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.3842 - val_loss: 0.3794
Epoch 292/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.3835 - val_loss: 0.1885
Epoch 293/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.5568 - val_loss: 0.2272
Epoch 294/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.2366 - val_loss: 0.3628
Epoch 295/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.5458 - val_loss: 0.2561
Epoch 296/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.3127 - val_loss: 0.1622
Epoch 297/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.2576 - val_loss: 0.3122
Epoch 298/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.2810 - val_loss: 0.2176
Epoch 299/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.2867 - val_loss: 1.3051
Epoch 300/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.5521 - val_loss: 0.2558
Epoch 301/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.6692 - val_loss: 2.4660
Epoch 302/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.3079 - val_loss: 0.1847
Epoch 303/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.3328 - val_loss: 0.3259
Epoch 304/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.2076 - val_loss: 0.5820
Epoch 305/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.3806 - val_loss: 0.2453
Epoch 306/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.3200 - val_loss: 0.9052
Epoch 307/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.4105 - val_loss: 0.2222
Epoch 308/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.3260 - val_loss: 0.2016
Epoch 309/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.4173 - val_loss: 0.2092
Epoch 310/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.2555 - val_loss: 0.2004
Epoch 311/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.4442 - val_loss: 0.8916
Epoch 312/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.3581 - val_loss: 0.3374
Epoch 313/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.3195 - val_loss: 0.1720
Epoch 314/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.5692 - val_loss: 0.1761
Epoch 315/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.2894 - val_loss: 0.9929
Epoch 316/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.2409 - val_loss: 0.2485
Epoch 317/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.2499 - val_loss: 1.7151
Epoch 318/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.4223 - val_loss: 0.1690
Epoch 319/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.4611 - val_loss: 0.4686
Epoch 320/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.3102 - val_loss: 0.6225
Epoch 321/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.3214 - val_loss: 0.6102
Epoch 322/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.3736 - val_loss: 0.2135
Epoch 323/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.2744 - val_loss: 0.5275
Epoch 324/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.3305 - val_loss: 0.5360
Epoch 325/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.4953 - val_loss: 1.5719
Epoch 326/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.2914 - val_loss: 0.2915
Epoch 327/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.5582 - val_loss: 0.2534
Epoch 328/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.1686 - val_loss: 0.1849
Epoch 329/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.3183 - val_loss: 0.2378
Epoch 330/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 0.2638 - val_loss: 0.2549
Epoch 331/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.4683 - val_loss: 0.5738
Epoch 332/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.2462 - val_loss: 0.3669
Epoch 333/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.3551 - val_loss: 0.3539
Epoch 334/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.3676 - val_loss: 0.2149
Epoch 335/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.3822 - val_loss: 0.2677
Epoch 336/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.3148 - val_loss: 0.1874
Epoch 337/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.3052 - val_loss: 0.1729
Epoch 338/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.3294 - val_loss: 0.5296
Epoch 339/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.2549 - val_loss: 0.1566
Epoch 340/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.6803 - val_loss: 0.1528
Epoch 341/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.2820 - val_loss: 0.1621
Epoch 342/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.2569 - val_loss: 0.2948
Epoch 343/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.2777 - val_loss: 0.1793
Epoch 344/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.2643 - val_loss: 0.1675
Epoch 345/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 1.0457 - val_loss: 0.2280
Epoch 346/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.1634 - val_loss: 0.2097
Epoch 347/1000
3888/3888 [==============================] - 0s 108us/sample - loss: 0.2629 - val_loss: 0.1523
Epoch 348/1000
3888/3888 [==============================] - 0s 108us/sample - loss: 0.2267 - val_loss: 0.3109
Epoch 349/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 0.3566 - val_loss: 0.5713
Epoch 350/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.4100 - val_loss: 0.3455
Epoch 351/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.2356 - val_loss: 0.2165
Epoch 352/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.3050 - val_loss: 0.2068
Epoch 353/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.3370 - val_loss: 0.1490
Epoch 354/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.2280 - val_loss: 0.2326
Epoch 355/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.2450 - val_loss: 0.4718
Epoch 356/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.5532 - val_loss: 0.1571
Epoch 357/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.4088 - val_loss: 0.1935
Epoch 358/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 0.2749 - val_loss: 0.3091
Epoch 359/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 0.2141 - val_loss: 2.6155
Epoch 360/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 0.4179 - val_loss: 0.1446
Epoch 361/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.1693 - val_loss: 0.2741
Epoch 362/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.3763 - val_loss: 0.2877
Epoch 363/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.2834 - val_loss: 0.4913
Epoch 364/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.4395 - val_loss: 0.1369
Epoch 365/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.1879 - val_loss: 0.3937
Epoch 366/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.5547 - val_loss: 0.4815
Epoch 367/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.2271 - val_loss: 0.1674
Epoch 368/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.6336 - val_loss: 0.2553
Epoch 369/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.2331 - val_loss: 0.1477
Epoch 370/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.1887 - val_loss: 0.2659
Epoch 371/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.1769 - val_loss: 0.2451
Epoch 372/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.4905 - val_loss: 0.1483
Epoch 373/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.2491 - val_loss: 1.4189
Epoch 374/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.3997 - val_loss: 0.5958
Epoch 375/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.1781 - val_loss: 0.3190
Epoch 376/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.3223 - val_loss: 0.3323
Epoch 377/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.3008 - val_loss: 0.2113
Epoch 378/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.2688 - val_loss: 0.1849
Epoch 379/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.2543 - val_loss: 0.3723
Epoch 380/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.3643 - val_loss: 0.4242
Epoch 381/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.6252 - val_loss: 0.2200
Epoch 382/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.2029 - val_loss: 0.1415
Epoch 383/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.3383 - val_loss: 0.1782
Epoch 384/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.3775 - val_loss: 0.1580
Epoch 385/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.1713 - val_loss: 0.2144
Epoch 386/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.3011 - val_loss: 0.1277
Epoch 387/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.3260 - val_loss: 0.1441
Epoch 388/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.5931 - val_loss: 0.1462
Epoch 389/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.2501 - val_loss: 0.1594
Epoch 390/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 0.2001 - val_loss: 0.6000
Epoch 391/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.3451 - val_loss: 0.1337
Epoch 392/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.4132 - val_loss: 0.7836
Epoch 393/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.1698 - val_loss: 0.2283
Epoch 394/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.2597 - val_loss: 0.1958
Epoch 395/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.2388 - val_loss: 0.5857
Epoch 396/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.4306 - val_loss: 0.4839
Epoch 397/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.2447 - val_loss: 0.2404
Epoch 398/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.4029 - val_loss: 0.1449
Epoch 399/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.2828 - val_loss: 0.1557
Epoch 400/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.1864 - val_loss: 0.3945
Epoch 401/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 0.4236 - val_loss: 0.8957
Epoch 402/1000
3888/3888 [==============================] - 0s 108us/sample - loss: 0.2442 - val_loss: 0.2266
Epoch 403/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.2858 - val_loss: 1.3159
Epoch 404/1000
3888/3888 [==============================] - 0s 106us/sample - loss: 0.2758 - val_loss: 0.1848
Epoch 405/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 0.2350 - val_loss: 0.1491
Epoch 406/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 0.7253 - val_loss: 0.3110
Epoch 407/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 0.1595 - val_loss: 0.1586
Epoch 408/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 0.2119 - val_loss: 0.1952
Epoch 409/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 0.2796 - val_loss: 0.2310
Epoch 410/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.4064 - val_loss: 0.1475
Epoch 411/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.2106 - val_loss: 0.1843
Epoch 412/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.2952 - val_loss: 0.1461
Epoch 413/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.3154 - val_loss: 0.1183
Epoch 414/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.2349 - val_loss: 0.5241
Epoch 415/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 0.2721 - val_loss: 0.2537
Epoch 416/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.4350 - val_loss: 0.2941
Epoch 417/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.2948 - val_loss: 0.1705
Epoch 418/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.3082 - val_loss: 0.1806
Epoch 419/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.1497 - val_loss: 0.1812
Epoch 420/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.3611 - val_loss: 0.1523
Epoch 421/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.5096 - val_loss: 0.3445
Epoch 422/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.1854 - val_loss: 0.1631
Epoch 423/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.3808 - val_loss: 0.8352
Epoch 424/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.2614 - val_loss: 0.1819
Epoch 425/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.1556 - val_loss: 0.2947
Epoch 426/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.2776 - val_loss: 0.1609
Epoch 427/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.2473 - val_loss: 6.5086
Epoch 428/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.5162 - val_loss: 0.2375
Epoch 429/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.2138 - val_loss: 0.1557
Epoch 430/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.1518 - val_loss: 0.1666
Epoch 431/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.4123 - val_loss: 0.4097
Epoch 432/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.2520 - val_loss: 0.1487
Epoch 433/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.2125 - val_loss: 0.7565
Epoch 434/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.3642 - val_loss: 0.1863
Epoch 435/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.2973 - val_loss: 0.1553
Epoch 436/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.3322 - val_loss: 0.1894
Epoch 437/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.2354 - val_loss: 0.2549
Epoch 438/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.3874 - val_loss: 0.7970
Epoch 439/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.2733 - val_loss: 0.4693
Epoch 440/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.2804 - val_loss: 0.1339
Epoch 441/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.2992 - val_loss: 0.1933
Epoch 442/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.3579 - val_loss: 0.6725
Epoch 443/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.2456 - val_loss: 0.1213
Epoch 444/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.5065 - val_loss: 0.1683
Epoch 445/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.1760 - val_loss: 0.7920
Epoch 446/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.1805 - val_loss: 0.2318
Epoch 447/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.2686 - val_loss: 1.0969
Epoch 448/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.7978 - val_loss: 0.1065
Epoch 449/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.1744 - val_loss: 0.1161
Epoch 450/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.1513 - val_loss: 0.1948
Epoch 451/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.2213 - val_loss: 0.3021
Epoch 452/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.2769 - val_loss: 0.7772
Epoch 453/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.2520 - val_loss: 0.0964
Epoch 454/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.1834 - val_loss: 0.2704
Epoch 455/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.5091 - val_loss: 0.2693
Epoch 456/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.1670 - val_loss: 0.1048
Epoch 457/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.2408 - val_loss: 0.4624
Epoch 458/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.2231 - val_loss: 0.5321
Epoch 459/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.3421 - val_loss: 0.1725
Epoch 460/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.6578 - val_loss: 0.1178
Epoch 461/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.1462 - val_loss: 0.1687
Epoch 462/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.1669 - val_loss: 0.1088
Epoch 463/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.2523 - val_loss: 0.2806
Epoch 464/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 0.1608 - val_loss: 0.1107
Epoch 465/1000
3888/3888 [==============================] - 0s 106us/sample - loss: 0.3310 - val_loss: 0.1148
Epoch 466/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.4867 - val_loss: 0.1165
Epoch 467/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.1935 - val_loss: 0.1175
Epoch 468/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.1562 - val_loss: 0.1751
Epoch 469/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.3961 - val_loss: 0.1083
Epoch 470/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.1986 - val_loss: 0.1122
Epoch 471/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.2485 - val_loss: 0.6166
Epoch 472/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.4213 - val_loss: 0.1524
Epoch 473/1000
3888/3888 [==============================] - 0s 108us/sample - loss: 0.2002 - val_loss: 0.1236
Epoch 474/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.3508 - val_loss: 1.5711
Epoch 475/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.1837 - val_loss: 0.1309
Epoch 476/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.2277 - val_loss: 0.1131
Epoch 477/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.2975 - val_loss: 0.2854
Epoch 478/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.2614 - val_loss: 0.5924
Epoch 479/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.3192 - val_loss: 0.1016
Epoch 480/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 0.5707 - val_loss: 0.1934
Epoch 481/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 0.1189 - val_loss: 0.1644
Epoch 482/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.2568 - val_loss: 0.1097
Epoch 483/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.1757 - val_loss: 0.4679
Epoch 484/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.2128 - val_loss: 0.1927
Epoch 485/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.2827 - val_loss: 0.2093
Epoch 486/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.3117 - val_loss: 0.1397
Epoch 487/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.3040 - val_loss: 0.4850
Epoch 488/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.2419 - val_loss: 0.0983
Epoch 489/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.3133 - val_loss: 0.1047
Epoch 490/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.2546 - val_loss: 0.1812
Epoch 491/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.1543 - val_loss: 0.1438
Epoch 492/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.3562 - val_loss: 0.5370
Epoch 493/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.2411 - val_loss: 0.2459
Epoch 494/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.2796 - val_loss: 0.1305
Epoch 495/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.2355 - val_loss: 0.1168
Epoch 496/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.1503 - val_loss: 0.3642
Epoch 497/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.4564 - val_loss: 0.1099
Epoch 498/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.3604 - val_loss: 0.2479
Epoch 499/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.1472 - val_loss: 0.0989
Epoch 500/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.2040 - val_loss: 0.7074
Epoch 501/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.2872 - val_loss: 0.2423
Epoch 502/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.2268 - val_loss: 1.9836
Epoch 503/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.3945 - val_loss: 0.1856
Epoch 504/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.2157 - val_loss: 0.7675
Epoch 505/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.2056 - val_loss: 0.1101
Epoch 506/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.5522 - val_loss: 0.1454
Epoch 507/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.1237 - val_loss: 0.0859
Epoch 508/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.1996 - val_loss: 0.1441
Epoch 509/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.3209 - val_loss: 1.6737
Epoch 510/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.3048 - val_loss: 0.2290
Epoch 511/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.1731 - val_loss: 0.0884
Epoch 512/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.3418 - val_loss: 0.2255
Epoch 513/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.2397 - val_loss: 0.1540
Epoch 514/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.4559 - val_loss: 0.2381
Epoch 515/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.1352 - val_loss: 0.1025
Epoch 516/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.2467 - val_loss: 0.3032
Epoch 517/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.2010 - val_loss: 0.1537
Epoch 518/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.2950 - val_loss: 0.2778
Epoch 519/1000
3888/3888 [==============================] - 0s 118us/sample - loss: 0.2308 - val_loss: 0.1241
Epoch 520/1000
3888/3888 [==============================] - 0s 118us/sample - loss: 0.2325 - val_loss: 0.1332
Epoch 521/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.2722 - val_loss: 0.3358
Epoch 522/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.2279 - val_loss: 0.1489
Epoch 523/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.3582 - val_loss: 0.1367
Epoch 524/1000
3888/3888 [==============================] - 0s 99us/sample - loss: 0.2329 - val_loss: 0.2969
Epoch 525/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.2159 - val_loss: 0.3246
Epoch 526/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.1950 - val_loss: 0.3147
Epoch 527/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.3956 - val_loss: 7.3494
Epoch 528/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.3437 - val_loss: 0.0881
Epoch 529/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.1196 - val_loss: 0.1449
Epoch 530/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.4482 - val_loss: 0.1258
Epoch 531/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.1242 - val_loss: 0.6483
Epoch 532/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.2776 - val_loss: 0.1437
Epoch 533/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.2503 - val_loss: 1.2579
Epoch 534/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.3776 - val_loss: 0.3604
Epoch 535/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.1576 - val_loss: 0.1602
Epoch 536/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.1559 - val_loss: 1.1819
Epoch 537/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.2492 - val_loss: 1.3400
Epoch 538/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.2191 - val_loss: 0.2714
Epoch 539/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.2219 - val_loss: 0.2076
Epoch 540/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.4030 - val_loss: 0.3182
Epoch 541/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.3296 - val_loss: 0.2039
Epoch 542/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.1752 - val_loss: 0.0873
Epoch 543/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.1601 - val_loss: 0.1252
Epoch 544/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.3413 - val_loss: 0.1242
Epoch 545/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.3152 - val_loss: 0.5218
Epoch 546/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.4251 - val_loss: 0.0797
Epoch 547/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.1196 - val_loss: 0.1614
Epoch 548/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.1196 - val_loss: 0.1528
Epoch 549/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.2530 - val_loss: 0.1248
Epoch 550/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.1898 - val_loss: 0.1503
Epoch 551/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.3436 - val_loss: 0.1489
Epoch 552/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.6686 - val_loss: 0.0900
Epoch 553/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.1087 - val_loss: 0.0915
Epoch 554/1000
3888/3888 [==============================] - 0s 119us/sample - loss: 0.2307 - val_loss: 0.1497
Epoch 555/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.1315 - val_loss: 0.1125
Epoch 556/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.2178 - val_loss: 0.0999
Epoch 557/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.2228 - val_loss: 0.1494
Epoch 558/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.2624 - val_loss: 0.1095
Epoch 559/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.3272 - val_loss: 0.0776
Epoch 560/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.1515 - val_loss: 0.0950
Epoch 561/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.3040 - val_loss: 0.2647
Epoch 562/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.1793 - val_loss: 0.1014
Epoch 563/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.3359 - val_loss: 0.1184
Epoch 564/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.1899 - val_loss: 0.0942
Epoch 565/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.3715 - val_loss: 0.1087
Epoch 566/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.2037 - val_loss: 0.0782
Epoch 567/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.3112 - val_loss: 0.0978
Epoch 568/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.2408 - val_loss: 0.3924
Epoch 569/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.1590 - val_loss: 1.3941
Epoch 570/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.2905 - val_loss: 0.1515
Epoch 571/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.1788 - val_loss: 0.1330
Epoch 572/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.7504 - val_loss: 0.1255
Epoch 573/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.1324 - val_loss: 0.0807
Epoch 574/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.3505 - val_loss: 0.1954
Epoch 575/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.1591 - val_loss: 0.1588
Epoch 576/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.1638 - val_loss: 0.1217
Epoch 577/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.1684 - val_loss: 1.1948
Epoch 578/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.3209 - val_loss: 2.4540
Epoch 579/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.2027 - val_loss: 0.2902
Epoch 580/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.1824 - val_loss: 1.1407
Epoch 581/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.7238 - val_loss: 0.7343
Epoch 582/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.2528 - val_loss: 0.1349
Epoch 583/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.0870 - val_loss: 0.1277
Epoch 584/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.1854 - val_loss: 0.0752
Epoch 585/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.1600 - val_loss: 0.2389
Epoch 586/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.2054 - val_loss: 0.2099
Epoch 587/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.1946 - val_loss: 0.0964
Epoch 588/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.1801 - val_loss: 0.1505
Epoch 589/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.3357 - val_loss: 0.2940
Epoch 590/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.1841 - val_loss: 0.0980
Epoch 591/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.2601 - val_loss: 1.4146
Epoch 592/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.2359 - val_loss: 0.2659
Epoch 593/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.3359 - val_loss: 0.9762
Epoch 594/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.2604 - val_loss: 0.2707
Epoch 595/1000
3888/3888 [==============================] - 0s 118us/sample - loss: 0.1782 - val_loss: 0.0883
Epoch 596/1000
3888/3888 [==============================] - 0s 118us/sample - loss: 0.1563 - val_loss: 0.3305
Epoch 597/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.3133 - val_loss: 0.1402
Epoch 598/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.2273 - val_loss: 0.0909
Epoch 599/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.2835 - val_loss: 0.3548
Epoch 600/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.1437 - val_loss: 0.1741
Epoch 601/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.5701 - val_loss: 0.0724
Epoch 602/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.2331 - val_loss: 0.0757
Epoch 603/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.1205 - val_loss: 0.1547
Epoch 604/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.1852 - val_loss: 0.0996
Epoch 605/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.1500 - val_loss: 1.2783
Epoch 606/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.3526 - val_loss: 0.1634
Epoch 607/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.2808 - val_loss: 0.2861
Epoch 608/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.1209 - val_loss: 0.1037
Epoch 609/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.2524 - val_loss: 0.2760
Epoch 610/1000
3888/3888 [==============================] - 0s 118us/sample - loss: 0.1978 - val_loss: 1.0580
Epoch 611/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.3811 - val_loss: 0.1306
Epoch 612/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.1923 - val_loss: 0.0874
Epoch 613/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.1602 - val_loss: 2.0825
Epoch 614/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.2804 - val_loss: 0.0988
Epoch 615/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.1910 - val_loss: 0.1996
Epoch 616/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.2529 - val_loss: 0.0950
Epoch 617/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.2094 - val_loss: 0.1801
Epoch 618/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.2606 - val_loss: 0.2317
Epoch 619/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.1509 - val_loss: 0.3306
Epoch 620/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.2635 - val_loss: 1.3363
Epoch 621/1000
3888/3888 [==============================] - 0s 118us/sample - loss: 0.2510 - val_loss: 0.1044
Epoch 622/1000
3888/3888 [==============================] - 0s 119us/sample - loss: 0.2769 - val_loss: 0.0698
Epoch 623/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.2682 - val_loss: 0.0758
Epoch 624/1000
3888/3888 [==============================] - 0s 122us/sample - loss: 0.2395 - val_loss: 0.0754
Epoch 625/1000
3888/3888 [==============================] - 0s 120us/sample - loss: 0.1595 - val_loss: 0.0751
Epoch 626/1000
3888/3888 [==============================] - 0s 121us/sample - loss: 0.3488 - val_loss: 0.2506
Epoch 627/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.2114 - val_loss: 0.3729
Epoch 628/1000
3888/3888 [==============================] - 0s 120us/sample - loss: 0.3222 - val_loss: 0.0774
Epoch 629/1000
3888/3888 [==============================] - 0s 119us/sample - loss: 0.1160 - val_loss: 0.0774
Epoch 630/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.2245 - val_loss: 1.0920
Epoch 631/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.1637 - val_loss: 0.7492
Epoch 632/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.2871 - val_loss: 0.0790
Epoch 633/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.2022 - val_loss: 0.0768
Epoch 634/1000
3888/3888 [==============================] - 0s 101us/sample - loss: 0.4095 - val_loss: 0.0741
Epoch 635/1000
3888/3888 [==============================] - 0s 108us/sample - loss: 0.1436 - val_loss: 0.1450
Epoch 636/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.2784 - val_loss: 0.1240
Epoch 637/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 0.3168 - val_loss: 0.1197
Epoch 638/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 0.1192 - val_loss: 0.1027
Epoch 639/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.2754 - val_loss: 0.1046
Epoch 640/1000
3888/3888 [==============================] - 0s 108us/sample - loss: 0.2155 - val_loss: 0.1262
Epoch 641/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 0.2931 - val_loss: 0.0998
Epoch 642/1000
3888/3888 [==============================] - 0s 106us/sample - loss: 0.2829 - val_loss: 0.2800
Epoch 643/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 0.1867 - val_loss: 0.0856
Epoch 644/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.1415 - val_loss: 0.0848
Epoch 645/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 0.2062 - val_loss: 0.1192
Epoch 646/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 0.2964 - val_loss: 0.9496
Epoch 647/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.3719 - val_loss: 0.0943
Epoch 648/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 0.2579 - val_loss: 0.0845
Epoch 649/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.1479 - val_loss: 0.1502
Epoch 650/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.2403 - val_loss: 0.0886
Epoch 651/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.1478 - val_loss: 0.1243
Epoch 652/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.1717 - val_loss: 1.0306
Epoch 653/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.3565 - val_loss: 0.8566
Epoch 654/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.1678 - val_loss: 0.1520
Epoch 655/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.3637 - val_loss: 0.0870
Epoch 656/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.0890 - val_loss: 0.0814
Epoch 657/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.2048 - val_loss: 0.8283
Epoch 658/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.4130 - val_loss: 0.0890
Epoch 659/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.2712 - val_loss: 11.0017
Epoch 660/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.2800 - val_loss: 0.0646
Epoch 661/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.2036 - val_loss: 0.1236
Epoch 662/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.1792 - val_loss: 0.3171
Epoch 663/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.1702 - val_loss: 0.2309
Epoch 664/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.1761 - val_loss: 0.0936
Epoch 665/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.3059 - val_loss: 0.0795
Epoch 666/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.1339 - val_loss: 0.5195
Epoch 667/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.3182 - val_loss: 0.7800
Epoch 668/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.1705 - val_loss: 0.1097
Epoch 669/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.3370 - val_loss: 0.1058
Epoch 670/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.1714 - val_loss: 0.0946
Epoch 671/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.2334 - val_loss: 0.1269
Epoch 672/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.1296 - val_loss: 0.6107
Epoch 673/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.3355 - val_loss: 1.4171
Epoch 674/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.2077 - val_loss: 0.0893
Epoch 675/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.3296 - val_loss: 0.0989
Epoch 676/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.1053 - val_loss: 0.0674
Epoch 677/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.1628 - val_loss: 0.0900
Epoch 678/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.2998 - val_loss: 0.1535
Epoch 679/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.2699 - val_loss: 0.0844
Epoch 680/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.1186 - val_loss: 0.1922
Epoch 681/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.1664 - val_loss: 0.1743
Epoch 682/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 0.2901 - val_loss: 0.3231
Epoch 683/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.2731 - val_loss: 0.3195
Epoch 684/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.1579 - val_loss: 0.1066
Epoch 685/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.2696 - val_loss: 0.5095
Epoch 686/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.1651 - val_loss: 0.1047
Epoch 687/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.2215 - val_loss: 0.3594
Epoch 688/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.3110 - val_loss: 0.1079
Epoch 689/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.1413 - val_loss: 0.0932
Epoch 690/1000
3888/3888 [==============================] - 0s 111us/sample - loss: 0.5393 - val_loss: 1.0025
Epoch 691/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.1635 - val_loss: 0.0791
Epoch 692/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.1216 - val_loss: 0.1672
Epoch 693/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.1872 - val_loss: 1.9027
Epoch 694/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.3784 - val_loss: 0.1756
Epoch 695/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.0907 - val_loss: 0.1522
Epoch 696/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.3989 - val_loss: 0.4528
Epoch 697/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.1232 - val_loss: 1.3107
Epoch 698/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.1884 - val_loss: 0.0924
Epoch 699/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.1757 - val_loss: 0.0892
Epoch 700/1000
3888/3888 [==============================] - 0s 109us/sample - loss: 0.2306 - val_loss: 0.8284
Epoch 701/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.2986 - val_loss: 0.0940
Epoch 702/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.3809 - val_loss: 0.0645
Epoch 703/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.1149 - val_loss: 0.1171
Epoch 704/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.3491 - val_loss: 0.1278
Epoch 705/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.0949 - val_loss: 0.1971
Epoch 706/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.1540 - val_loss: 0.0819
Epoch 707/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.1636 - val_loss: 0.1790
Epoch 708/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.3759 - val_loss: 0.0761
Epoch 709/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.1004 - val_loss: 0.5051
Epoch 710/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.3742 - val_loss: 0.0927
Epoch 711/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.0919 - val_loss: 0.0770
Epoch 712/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.2373 - val_loss: 0.0684
Epoch 713/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.2051 - val_loss: 0.2519
Epoch 714/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.1275 - val_loss: 0.1482
Epoch 715/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.2786 - val_loss: 0.0798
Epoch 716/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.1782 - val_loss: 0.1037
Epoch 717/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.2454 - val_loss: 0.3674
Epoch 718/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.2237 - val_loss: 0.0922
Epoch 719/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.2857 - val_loss: 2.2270
Epoch 720/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.3365 - val_loss: 0.0757
Epoch 721/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.1209 - val_loss: 0.2106
Epoch 722/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.2216 - val_loss: 0.4283
Epoch 723/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.1889 - val_loss: 0.1084
Epoch 724/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.1584 - val_loss: 0.3512
Epoch 725/1000
3888/3888 [==============================] - 0s 118us/sample - loss: 0.2541 - val_loss: 0.0756
Epoch 726/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.2104 - val_loss: 1.1149
Epoch 727/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.4546 - val_loss: 0.1372
Epoch 728/1000
3888/3888 [==============================] - 0s 119us/sample - loss: 0.0930 - val_loss: 0.1230
Epoch 729/1000
3888/3888 [==============================] - 0s 120us/sample - loss: 0.2025 - val_loss: 0.2054
Epoch 730/1000
3888/3888 [==============================] - 0s 119us/sample - loss: 0.1965 - val_loss: 0.1469
Epoch 731/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.1679 - val_loss: 0.1350
Epoch 732/1000
3888/3888 [==============================] - 0s 119us/sample - loss: 0.3183 - val_loss: 0.2085
Epoch 733/1000
3888/3888 [==============================] - 0s 107us/sample - loss: 0.1354 - val_loss: 0.1995
Epoch 734/1000
3888/3888 [==============================] - 0s 110us/sample - loss: 0.4262 - val_loss: 0.1180
Epoch 735/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.0822 - val_loss: 0.0714
Epoch 736/1000
3888/3888 [==============================] - 0s 119us/sample - loss: 0.1160 - val_loss: 0.0657
Epoch 737/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.3000 - val_loss: 2.2415
Epoch 738/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.3901 - val_loss: 0.0762
Epoch 739/1000
3888/3888 [==============================] - 0s 119us/sample - loss: 0.0749 - val_loss: 0.0842
Epoch 740/1000
3888/3888 [==============================] - 0s 119us/sample - loss: 0.2134 - val_loss: 0.0962
Epoch 741/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.2142 - val_loss: 0.0832
Epoch 742/1000
3888/3888 [==============================] - 0s 120us/sample - loss: 0.3354 - val_loss: 0.0695
Epoch 743/1000
3888/3888 [==============================] - 0s 120us/sample - loss: 0.1033 - val_loss: 0.0970
Epoch 744/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.3971 - val_loss: 0.1142
Epoch 745/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.1268 - val_loss: 0.0800
Epoch 746/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.1142 - val_loss: 0.6437
Epoch 747/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.1911 - val_loss: 0.0937
Epoch 748/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.2800 - val_loss: 0.0614
Epoch 749/1000
3888/3888 [==============================] - 0s 120us/sample - loss: 0.1228 - val_loss: 0.1064
Epoch 750/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.3614 - val_loss: 0.0861
Epoch 751/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.1369 - val_loss: 0.1685
Epoch 752/1000
3888/3888 [==============================] - 0s 120us/sample - loss: 0.1699 - val_loss: 0.1814
Epoch 753/1000
3888/3888 [==============================] - 0s 118us/sample - loss: 0.2921 - val_loss: 0.4110
Epoch 754/1000
3888/3888 [==============================] - 0s 120us/sample - loss: 0.1310 - val_loss: 0.2202
Epoch 755/1000
3888/3888 [==============================] - 0s 118us/sample - loss: 0.2092 - val_loss: 0.1008
Epoch 756/1000
3888/3888 [==============================] - 0s 119us/sample - loss: 0.2689 - val_loss: 0.1771
Epoch 757/1000
3888/3888 [==============================] - 0s 118us/sample - loss: 0.2158 - val_loss: 0.0685
Epoch 758/1000
3888/3888 [==============================] - 0s 120us/sample - loss: 0.1553 - val_loss: 0.6060
Epoch 759/1000
3888/3888 [==============================] - 0s 118us/sample - loss: 0.2235 - val_loss: 1.8171
Epoch 760/1000
3888/3888 [==============================] - 0s 118us/sample - loss: 0.2023 - val_loss: 0.2343
Epoch 761/1000
3888/3888 [==============================] - 0s 118us/sample - loss: 0.3096 - val_loss: 0.0948
Epoch 762/1000
3888/3888 [==============================] - 0s 118us/sample - loss: 0.1600 - val_loss: 0.0636
Epoch 763/1000
3888/3888 [==============================] - 0s 119us/sample - loss: 0.1146 - val_loss: 0.3618
Epoch 764/1000
3888/3888 [==============================] - 0s 119us/sample - loss: 0.3212 - val_loss: 0.0661
Epoch 765/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.2220 - val_loss: 0.0773
Epoch 766/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.4659 - val_loss: 0.2733
Epoch 767/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.0728 - val_loss: 0.0663
Epoch 768/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.1859 - val_loss: 0.0798
Epoch 769/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.1405 - val_loss: 0.1045
Epoch 770/1000
3888/3888 [==============================] - 0s 118us/sample - loss: 0.5147 - val_loss: 1.3501
Epoch 771/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.1034 - val_loss: 0.0671
Epoch 772/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.1266 - val_loss: 0.0572
Epoch 773/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.1371 - val_loss: 0.1061
Epoch 774/1000
3888/3888 [==============================] - 0s 119us/sample - loss: 0.3061 - val_loss: 0.0684
Epoch 775/1000
3888/3888 [==============================] - 0s 118us/sample - loss: 0.0758 - val_loss: 0.0634
Epoch 776/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.3489 - val_loss: 0.1306
Epoch 777/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.1307 - val_loss: 0.0813
Epoch 778/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.1807 - val_loss: 0.1649
Epoch 779/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.2237 - val_loss: 1.8152
Epoch 780/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.1745 - val_loss: 0.0949
Epoch 781/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.2468 - val_loss: 0.1705
Epoch 782/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.1137 - val_loss: 0.7684
Epoch 783/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.3673 - val_loss: 0.1360
Epoch 784/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.0986 - val_loss: 0.2973
Epoch 785/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.2639 - val_loss: 0.3746
Epoch 786/1000
3888/3888 [==============================] - 0s 119us/sample - loss: 0.1797 - val_loss: 1.7802
Epoch 787/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.2062 - val_loss: 0.1072
Epoch 788/1000
3888/3888 [==============================] - 0s 119us/sample - loss: 0.1067 - val_loss: 0.2442
Epoch 789/1000
3888/3888 [==============================] - 0s 118us/sample - loss: 0.2788 - val_loss: 0.1460
Epoch 790/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.1539 - val_loss: 0.6291
Epoch 791/1000
3888/3888 [==============================] - 0s 119us/sample - loss: 0.1879 - val_loss: 0.0709
Epoch 792/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.4817 - val_loss: 1.3859
Epoch 793/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.1143 - val_loss: 0.0844
Epoch 794/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.1823 - val_loss: 0.1352
Epoch 795/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.2603 - val_loss: 0.1123
Epoch 796/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.0954 - val_loss: 0.1319
Epoch 797/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.2455 - val_loss: 0.0922
Epoch 798/1000
3888/3888 [==============================] - 0s 120us/sample - loss: 0.1473 - val_loss: 0.0696
Epoch 799/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.1855 - val_loss: 0.0917
Epoch 800/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.2594 - val_loss: 0.1642
Epoch 801/1000
3888/3888 [==============================] - 0s 123us/sample - loss: 0.1461 - val_loss: 0.0578
Epoch 802/1000
3888/3888 [==============================] - 0s 118us/sample - loss: 0.4558 - val_loss: 0.0568
Epoch 803/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.1232 - val_loss: 0.0516
Epoch 804/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.1454 - val_loss: 0.1457
Epoch 805/1000
3888/3888 [==============================] - 0s 118us/sample - loss: 0.2751 - val_loss: 0.0510
Epoch 806/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.1485 - val_loss: 0.1769
Epoch 807/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.1418 - val_loss: 0.0852
Epoch 808/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.2565 - val_loss: 0.0827
Epoch 809/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.1163 - val_loss: 0.1923
Epoch 810/1000
3888/3888 [==============================] - 0s 119us/sample - loss: 0.2136 - val_loss: 0.1623
Epoch 811/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.2176 - val_loss: 0.0700
Epoch 812/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.4820 - val_loss: 0.0732
Epoch 813/1000
3888/3888 [==============================] - 0s 118us/sample - loss: 0.1121 - val_loss: 0.0541
Epoch 814/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.1720 - val_loss: 0.3470
Epoch 815/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.1205 - val_loss: 0.1808
Epoch 816/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.2112 - val_loss: 0.0697
Epoch 817/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.2077 - val_loss: 0.1029
Epoch 818/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.3010 - val_loss: 0.2108
Epoch 819/1000
3888/3888 [==============================] - 0s 118us/sample - loss: 0.0849 - val_loss: 0.0562
Epoch 820/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.1624 - val_loss: 0.3852
Epoch 821/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.2306 - val_loss: 0.1650
Epoch 822/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.8358 - val_loss: 0.1328
Epoch 823/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.0834 - val_loss: 0.0783
Epoch 824/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.2108 - val_loss: 0.0845
Epoch 825/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.0779 - val_loss: 0.0878
Epoch 826/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.1430 - val_loss: 0.0663
Epoch 827/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.1883 - val_loss: 0.0892
Epoch 828/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.1681 - val_loss: 0.0564
Epoch 829/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.3370 - val_loss: 0.1253
Epoch 830/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.1476 - val_loss: 0.0696
Epoch 831/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.0802 - val_loss: 0.1129
Epoch 832/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.1985 - val_loss: 0.0917
Epoch 833/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.3157 - val_loss: 0.5762
Epoch 834/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.1232 - val_loss: 0.1640
Epoch 835/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.2095 - val_loss: 0.2085
Epoch 836/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.2180 - val_loss: 0.1537
Epoch 837/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.2545 - val_loss: 0.1015
Epoch 838/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.0856 - val_loss: 0.1532
Epoch 839/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.3511 - val_loss: 0.0935
Epoch 840/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.2789 - val_loss: 0.4443
Epoch 841/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.0760 - val_loss: 0.0718
Epoch 842/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.2409 - val_loss: 0.0730
Epoch 843/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.1292 - val_loss: 0.0483
Epoch 844/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.2122 - val_loss: 0.0731
Epoch 845/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.1212 - val_loss: 0.0755
Epoch 846/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.3623 - val_loss: 0.0610
Epoch 847/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.0904 - val_loss: 0.0584
Epoch 848/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.4007 - val_loss: 0.0651
Epoch 849/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.1434 - val_loss: 0.2775
Epoch 850/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.3900 - val_loss: 0.1619
Epoch 851/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.1121 - val_loss: 0.3051
Epoch 852/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.1336 - val_loss: 0.0998
Epoch 853/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.0942 - val_loss: 0.0700
Epoch 854/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.2651 - val_loss: 0.3456
Epoch 855/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.1263 - val_loss: 0.0738
Epoch 856/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.2104 - val_loss: 0.4709
Epoch 857/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.1399 - val_loss: 0.0925
Epoch 858/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.4500 - val_loss: 0.0723
Epoch 859/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.0988 - val_loss: 0.1748
Epoch 860/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.1238 - val_loss: 0.0631
Epoch 861/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.1344 - val_loss: 0.2982
Epoch 862/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.2085 - val_loss: 0.1733
Epoch 863/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.2137 - val_loss: 0.2621
Epoch 864/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.2446 - val_loss: 0.1470
Epoch 865/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.1332 - val_loss: 0.4672
Epoch 866/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.3418 - val_loss: 0.0542
Epoch 867/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.1685 - val_loss: 0.0580
Epoch 868/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.1020 - val_loss: 0.1579
Epoch 869/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.2826 - val_loss: 0.0702
Epoch 870/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.1841 - val_loss: 5.3069
Epoch 871/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.2151 - val_loss: 0.0502
Epoch 872/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.0866 - val_loss: 1.0961
Epoch 873/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.3490 - val_loss: 0.1516
Epoch 874/1000
3888/3888 [==============================] - 0s 118us/sample - loss: 0.1303 - val_loss: 0.1554
Epoch 875/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.1617 - val_loss: 0.0894
Epoch 876/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.3499 - val_loss: 0.0751
Epoch 877/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.0855 - val_loss: 0.0846
Epoch 878/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.3049 - val_loss: 0.1382
Epoch 879/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.0721 - val_loss: 0.0715
Epoch 880/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.5565 - val_loss: 0.2243
Epoch 881/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.1206 - val_loss: 0.0680
Epoch 882/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.1344 - val_loss: 0.0644
Epoch 883/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.2247 - val_loss: 0.0570
Epoch 884/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.0919 - val_loss: 0.0868
Epoch 885/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.1041 - val_loss: 1.4045
Epoch 886/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.1450 - val_loss: 0.1644
Epoch 887/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.4869 - val_loss: 0.0537
Epoch 888/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.0761 - val_loss: 0.0876
Epoch 889/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.1125 - val_loss: 0.0860
Epoch 890/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.2137 - val_loss: 0.0858
Epoch 891/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.1387 - val_loss: 0.2571
Epoch 892/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.1840 - val_loss: 0.0710
Epoch 893/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.2732 - val_loss: 0.0931
Epoch 894/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.0850 - val_loss: 0.0951
Epoch 895/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.3204 - val_loss: 0.0789
Epoch 896/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.1110 - val_loss: 0.5431
Epoch 897/1000
3888/3888 [==============================] - 0s 112us/sample - loss: 0.2259 - val_loss: 0.1011
Epoch 898/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.2424 - val_loss: 0.1982
Epoch 899/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.0882 - val_loss: 0.0662
Epoch 900/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.3083 - val_loss: 0.1104
Epoch 901/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.2779 - val_loss: 0.0964
Epoch 902/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.0793 - val_loss: 0.0783
Epoch 903/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.1236 - val_loss: 0.0902
Epoch 904/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.2208 - val_loss: 0.1078
Epoch 905/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.1604 - val_loss: 5.0204
Epoch 906/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.2848 - val_loss: 0.2597
Epoch 907/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.2453 - val_loss: 0.0750
Epoch 908/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.0978 - val_loss: 0.0637
Epoch 909/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.1507 - val_loss: 0.2213
Epoch 910/1000
3888/3888 [==============================] - 0s 118us/sample - loss: 0.1963 - val_loss: 0.2490
Epoch 911/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.1776 - val_loss: 0.1265
Epoch 912/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.2867 - val_loss: 0.1448
Epoch 913/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.1363 - val_loss: 0.3233
Epoch 914/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.2373 - val_loss: 0.1823
Epoch 915/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.2536 - val_loss: 0.0495
Epoch 916/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.1049 - val_loss: 0.3886
Epoch 917/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.1610 - val_loss: 0.2207
Epoch 918/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.2647 - val_loss: 0.6126
Epoch 919/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.1382 - val_loss: 0.1665
Epoch 920/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.1632 - val_loss: 0.1943
Epoch 921/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.1482 - val_loss: 0.5755
Epoch 922/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.2332 - val_loss: 0.0651
Epoch 923/1000
3888/3888 [==============================] - 0s 119us/sample - loss: 0.2460 - val_loss: 0.2919
Epoch 924/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.0934 - val_loss: 0.0668
Epoch 925/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.6473 - val_loss: 0.0789
Epoch 926/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.0716 - val_loss: 0.1687
Epoch 927/1000
3888/3888 [==============================] - 0s 118us/sample - loss: 0.0800 - val_loss: 0.8557
Epoch 928/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.3238 - val_loss: 0.0722
Epoch 929/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.0649 - val_loss: 0.1097
Epoch 930/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.1116 - val_loss: 0.2908
Epoch 931/1000
3888/3888 [==============================] - 0s 117us/sample - loss: 0.1877 - val_loss: 2.2144
Epoch 932/1000
3888/3888 [==============================] - 0s 118us/sample - loss: 0.3020 - val_loss: 0.2213
Epoch 933/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.1047 - val_loss: 0.1887
Epoch 934/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.1420 - val_loss: 0.1794
Epoch 935/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.2041 - val_loss: 0.0832
Epoch 936/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.3823 - val_loss: 0.0686
Epoch 937/1000
3888/3888 [==============================] - 0s 115us/sample - loss: 0.0563 - val_loss: 0.0967
Epoch 938/1000
3888/3888 [==============================] - 0s 113us/sample - loss: 0.1263 - val_loss: 0.1219
Epoch 939/1000
3888/3888 [==============================] - 0s 114us/sample - loss: 0.1903 - val_loss: 2.4087
Epoch 940/1000
3888/3888 [==============================] - 0s 119us/sample - loss: 0.3117 - val_loss: 0.0814
Epoch 941/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.3101 - val_loss: 0.1388
Epoch 942/1000
3888/3888 [==============================] - 0s 116us/sample - loss: 0.1111 - val_loss: 0.7598
Epoch 943/1000
3360/3888 [========================>.....] - ETA: 0s - loss: 0.1674Restoring model weights from the end of the best epoch.
3888/3888 [==============================] - 0s 116us/sample - loss: 0.1553 - val_loss: 0.0572
Epoch 00943: early stopping
In [55]:
print(history.history.keys())
print('best value: ', autoencoder.evaluate(X_train_pca, X_train_pca, verbose=0))


pd.DataFrame(history.history).plot(figsize=(8, 5), logy=True)
plt.grid()
dict_keys(['loss', 'val_loss'])
best value:  0.04827761372604979
In [56]:
X_reconstructions = autoencoder.predict(X_train_pca)
X_reconstructions = pca.inverse_transform(X_reconstructions)
calculateerror(X_train_1D.reshape(len(times),len(groups),nl,nc), 
               X_reconstructions.reshape(len(times),len(groups),nl,nc), 
               groups,
               print_step=0)
max_abs_error:  2.195369238521245
mean_abs_error:  0.026052220861696996
/home/viluiz/anaconda3/envs/py3ml/lib/python3.7/site-packages/ipykernel_launcher.py:3: RuntimeWarning: divide by zero encountered in true_divide
  This is separate from the ipykernel package so we can avoid doing imports until
/home/viluiz/anaconda3/envs/py3ml/lib/python3.7/site-packages/ipykernel_launcher.py:3: RuntimeWarning: invalid value encountered in true_divide
  This is separate from the ipykernel package so we can avoid doing imports until
In [57]:
fig, ax = plt.subplots(2,4, figsize=[20,10])
for i, group in enumerate(groups):
    im = ax.flatten()[i].imshow(X_reconstructions.reshape(len(times),len(groups),nl,nc)[100,i,:,:])
    fig.colorbar(im, ax=ax.flatten()[i])
    ax.flatten()[i].set_title(group)
In [58]:
fig, ax = plt.subplots(2,4, figsize=[20,10])
for i, group in enumerate(groups):
    ax.flatten()[i].plot(times, X_train_1D[:,i*nl*nc+4])
    ax.flatten()[i].plot(times, X_reconstructions[:,i*nl*nc+4],'--')
    ax.flatten()[i].set_title(group)

Convolutional autoencoder

In [59]:
#tf.keras.backend.set_image_data_format('channels_first') 
tf.keras.backend.image_data_format()
Out[59]:
'channels_last'
In [60]:
#from sklearn.model_selection import train_test_split
#X_train, X_valid = train_test_split(X_train_3D_norm, test_size=0.2, random_state=42)

X_train = np.moveaxis(X_train_3D_norm, 1, 3) # for channel last
In [61]:
tf.random.set_seed(42)
np.random.seed(42)

# Need to have validation loss
early_stopping = keras.callbacks.EarlyStopping(monitor='val_loss',
                                               min_delta=0.0,
                                               patience=100,
                                               verbose=2,
                                               restore_best_weights=True)

conv_encoder = keras.models.Sequential([
    #keras.layers.Reshape([28, 28, 1], input_shape=[28, 28]),
    keras.layers.InputLayer(input_shape=(10, 10, 8)),
    keras.layers.Conv2D(64, kernel_size=3, padding="SAME", activation="elu"),
    keras.layers.Flatten(),
    keras.layers.Dense(100, activation="elu"),
    keras.layers.Dense(50, activation="elu"),
    keras.layers.Dense(15)
])
conv_decoder = keras.models.Sequential([
    keras.layers.Dense(50, input_shape=[15], activation="elu"),
    keras.layers.Dense(100, activation="elu"),
    keras.layers.Dense(64*10*10, activation="elu"),
    keras.layers.Reshape(target_shape=(10, 10, 64)),
    keras.layers.Conv2DTranspose(64, kernel_size=3, strides=1, padding="SAME", activation="elu"), 
    keras.layers.Conv2DTranspose(8, kernel_size=3, strides=1, padding="SAME"),
    keras.layers.Reshape([10, 10, 8])
])

conv_ae = keras.models.Sequential([conv_encoder, conv_decoder])
conv_ae.compile(loss="mse", 
                optimizer=keras.optimizers.Nadam(lr=0.0001, beta_1=0.9, beta_2=0.999))

conv_encoder.summary()
conv_decoder.summary()
Model: "sequential_12"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, 10, 10, 64)        4672      
_________________________________________________________________
flatten (Flatten)            (None, 6400)              0         
_________________________________________________________________
dense_18 (Dense)             (None, 100)               640100    
_________________________________________________________________
dense_19 (Dense)             (None, 50)                5050      
_________________________________________________________________
dense_20 (Dense)             (None, 15)                765       
=================================================================
Total params: 650,587
Trainable params: 650,587
Non-trainable params: 0
_________________________________________________________________
Model: "sequential_13"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_21 (Dense)             (None, 50)                800       
_________________________________________________________________
dense_22 (Dense)             (None, 100)               5100      
_________________________________________________________________
dense_23 (Dense)             (None, 6400)              646400    
_________________________________________________________________
reshape (Reshape)            (None, 10, 10, 64)        0         
_________________________________________________________________
conv2d_transpose (Conv2DTran (None, 10, 10, 64)        36928     
_________________________________________________________________
conv2d_transpose_1 (Conv2DTr (None, 10, 10, 8)         4616      
_________________________________________________________________
reshape_1 (Reshape)          (None, 10, 10, 8)         0         
=================================================================
Total params: 693,844
Trainable params: 693,844
Non-trainable params: 0
_________________________________________________________________
In [62]:
history = conv_ae.fit(X_train, X_train, 
                      epochs=1000, 
                      validation_data=(X_train, X_train),
                      callbacks=[early_stopping])
Train on 3888 samples, validate on 3888 samples
Epoch 1/1000
3888/3888 [==============================] - 3s 895us/sample - loss: 0.0434 - val_loss: 0.0080
Epoch 2/1000
3888/3888 [==============================] - 2s 558us/sample - loss: 0.0036 - val_loss: 0.0015
Epoch 3/1000
3888/3888 [==============================] - 2s 522us/sample - loss: 0.0011 - val_loss: 7.2160e-04
Epoch 4/1000
3888/3888 [==============================] - 2s 527us/sample - loss: 6.3083e-04 - val_loss: 4.0945e-04
Epoch 5/1000
3888/3888 [==============================] - 2s 538us/sample - loss: 6.5230e-04 - val_loss: 3.1501e-04
Epoch 6/1000
3888/3888 [==============================] - 2s 535us/sample - loss: 3.0039e-04 - val_loss: 2.3431e-04
Epoch 7/1000
3888/3888 [==============================] - 2s 539us/sample - loss: 3.5650e-04 - val_loss: 1.9783e-04
Epoch 8/1000
3888/3888 [==============================] - 2s 533us/sample - loss: 2.3832e-04 - val_loss: 1.7775e-04
Epoch 9/1000
3888/3888 [==============================] - 2s 556us/sample - loss: 2.5771e-04 - val_loss: 1.7263e-04
Epoch 10/1000
3888/3888 [==============================] - 2s 537us/sample - loss: 2.9760e-04 - val_loss: 3.2514e-04
Epoch 11/1000
3888/3888 [==============================] - 2s 544us/sample - loss: 1.6701e-04 - val_loss: 1.2307e-04
Epoch 12/1000
3888/3888 [==============================] - 2s 531us/sample - loss: 1.8069e-04 - val_loss: 2.7333e-04
Epoch 13/1000
3888/3888 [==============================] - 2s 543us/sample - loss: 2.5737e-04 - val_loss: 8.7746e-05
Epoch 14/1000
3888/3888 [==============================] - 2s 640us/sample - loss: 1.2636e-04 - val_loss: 6.6394e-04
Epoch 15/1000
3888/3888 [==============================] - 2s 581us/sample - loss: 1.7777e-04 - val_loss: 8.4140e-05
Epoch 16/1000
3888/3888 [==============================] - 2s 549us/sample - loss: 2.2911e-04 - val_loss: 7.2874e-05
Epoch 17/1000
3888/3888 [==============================] - 2s 546us/sample - loss: 1.3529e-04 - val_loss: 7.1527e-05
Epoch 18/1000
3888/3888 [==============================] - 2s 519us/sample - loss: 9.6322e-05 - val_loss: 9.0245e-05
Epoch 19/1000
3888/3888 [==============================] - 2s 542us/sample - loss: 1.5794e-04 - val_loss: 6.4697e-05
Epoch 20/1000
3888/3888 [==============================] - 2s 531us/sample - loss: 1.0102e-04 - val_loss: 7.2958e-05
Epoch 21/1000
3888/3888 [==============================] - 2s 527us/sample - loss: 1.6049e-04 - val_loss: 5.9588e-05
Epoch 22/1000
3888/3888 [==============================] - 2s 523us/sample - loss: 9.6004e-05 - val_loss: 4.6323e-05
Epoch 23/1000
3888/3888 [==============================] - 2s 528us/sample - loss: 1.2193e-04 - val_loss: 7.0513e-05
Epoch 24/1000
3888/3888 [==============================] - 2s 532us/sample - loss: 9.5553e-05 - val_loss: 4.6434e-05
Epoch 25/1000
3888/3888 [==============================] - 2s 528us/sample - loss: 1.3025e-04 - val_loss: 6.7973e-05
Epoch 26/1000
3888/3888 [==============================] - 2s 529us/sample - loss: 7.9251e-05 - val_loss: 3.7723e-04
Epoch 27/1000
3888/3888 [==============================] - 2s 519us/sample - loss: 7.1520e-05 - val_loss: 4.5085e-05
Epoch 28/1000
3888/3888 [==============================] - 2s 538us/sample - loss: 1.5674e-04 - val_loss: 8.4231e-05
Epoch 29/1000
3888/3888 [==============================] - 2s 518us/sample - loss: 6.7590e-05 - val_loss: 5.9001e-05
Epoch 30/1000
3888/3888 [==============================] - 2s 523us/sample - loss: 9.6203e-05 - val_loss: 4.4551e-04
Epoch 31/1000
3888/3888 [==============================] - 2s 518us/sample - loss: 6.8562e-05 - val_loss: 5.6724e-05
Epoch 32/1000
3888/3888 [==============================] - 2s 524us/sample - loss: 1.1168e-04 - val_loss: 2.8260e-05
Epoch 33/1000
3888/3888 [==============================] - 2s 596us/sample - loss: 1.9677e-04 - val_loss: 3.2629e-05
Epoch 34/1000
3888/3888 [==============================] - 2s 562us/sample - loss: 3.0406e-05 - val_loss: 4.4957e-05
Epoch 35/1000
3888/3888 [==============================] - 2s 513us/sample - loss: 4.0286e-05 - val_loss: 3.0143e-05
Epoch 36/1000
3888/3888 [==============================] - 2s 530us/sample - loss: 9.4810e-05 - val_loss: 3.9579e-05
Epoch 37/1000
3888/3888 [==============================] - 2s 523us/sample - loss: 3.7689e-05 - val_loss: 3.2112e-05
Epoch 38/1000
3888/3888 [==============================] - 2s 503us/sample - loss: 1.5882e-04 - val_loss: 4.4055e-05
Epoch 39/1000
3888/3888 [==============================] - 2s 513us/sample - loss: 3.0080e-05 - val_loss: 2.1989e-05
Epoch 40/1000
3888/3888 [==============================] - 2s 516us/sample - loss: 3.5845e-05 - val_loss: 3.1032e-05
Epoch 41/1000
3888/3888 [==============================] - 2s 519us/sample - loss: 4.5945e-05 - val_loss: 2.4032e-05
Epoch 42/1000
3888/3888 [==============================] - 2s 518us/sample - loss: 6.9854e-05 - val_loss: 0.0019
Epoch 43/1000
3888/3888 [==============================] - 2s 513us/sample - loss: 1.2242e-04 - val_loss: 5.3096e-05
Epoch 44/1000
3888/3888 [==============================] - 2s 518us/sample - loss: 9.9203e-05 - val_loss: 2.8848e-05
Epoch 45/1000
3888/3888 [==============================] - 2s 523us/sample - loss: 2.2306e-05 - val_loss: 1.8452e-05
Epoch 46/1000
3888/3888 [==============================] - 2s 508us/sample - loss: 4.7209e-05 - val_loss: 2.0237e-05
Epoch 47/1000
3888/3888 [==============================] - 2s 516us/sample - loss: 3.3210e-05 - val_loss: 2.8410e-05
Epoch 48/1000
3888/3888 [==============================] - 2s 518us/sample - loss: 4.7201e-05 - val_loss: 2.2302e-05
Epoch 49/1000
3888/3888 [==============================] - 2s 525us/sample - loss: 1.7401e-04 - val_loss: 1.7304e-05
Epoch 50/1000
3888/3888 [==============================] - 2s 535us/sample - loss: 1.9281e-05 - val_loss: 2.1217e-05
Epoch 51/1000
3888/3888 [==============================] - 2s 521us/sample - loss: 4.9003e-05 - val_loss: 2.4669e-05
Epoch 52/1000
3888/3888 [==============================] - 2s 518us/sample - loss: 2.1840e-05 - val_loss: 2.0712e-05
Epoch 53/1000
3888/3888 [==============================] - 2s 525us/sample - loss: 3.2953e-05 - val_loss: 8.5942e-05
Epoch 54/1000
3888/3888 [==============================] - 2s 521us/sample - loss: 5.3268e-05 - val_loss: 1.5969e-05
Epoch 55/1000
3888/3888 [==============================] - 2s 530us/sample - loss: 1.4217e-04 - val_loss: 1.5205e-05
Epoch 56/1000
3888/3888 [==============================] - 2s 514us/sample - loss: 1.7671e-05 - val_loss: 1.8281e-05
Epoch 57/1000
3888/3888 [==============================] - 2s 515us/sample - loss: 2.3153e-05 - val_loss: 2.6244e-05
Epoch 58/1000
3888/3888 [==============================] - 2s 520us/sample - loss: 2.6138e-05 - val_loss: 6.9503e-04
Epoch 59/1000
3888/3888 [==============================] - 2s 519us/sample - loss: 4.4097e-05 - val_loss: 1.9275e-05
Epoch 60/1000
3888/3888 [==============================] - 2s 518us/sample - loss: 1.9835e-05 - val_loss: 1.2806e-05
Epoch 61/1000
3888/3888 [==============================] - 2s 526us/sample - loss: 3.8098e-05 - val_loss: 3.2059e-05
Epoch 62/1000
3888/3888 [==============================] - 2s 521us/sample - loss: 7.4090e-05 - val_loss: 3.2743e-05
Epoch 63/1000
3888/3888 [==============================] - 2s 519us/sample - loss: 2.0525e-05 - val_loss: 1.6393e-05
Epoch 64/1000
3888/3888 [==============================] - 2s 513us/sample - loss: 2.5792e-05 - val_loss: 1.4576e-05
Epoch 65/1000
3888/3888 [==============================] - 2s 522us/sample - loss: 3.6762e-05 - val_loss: 1.7539e-05
Epoch 66/1000
3888/3888 [==============================] - 2s 520us/sample - loss: 3.7106e-05 - val_loss: 1.4789e-05
Epoch 67/1000
3888/3888 [==============================] - 2s 521us/sample - loss: 1.3481e-04 - val_loss: 9.9734e-05
Epoch 68/1000
3888/3888 [==============================] - 2s 512us/sample - loss: 1.8228e-05 - val_loss: 1.2812e-05
Epoch 69/1000
3888/3888 [==============================] - 2s 515us/sample - loss: 2.3419e-05 - val_loss: 1.8164e-05
Epoch 70/1000
3888/3888 [==============================] - 2s 515us/sample - loss: 2.2865e-05 - val_loss: 1.2283e-05
Epoch 71/1000
3888/3888 [==============================] - 2s 520us/sample - loss: 1.1961e-05 - val_loss: 1.1199e-05
Epoch 72/1000
3888/3888 [==============================] - 2s 516us/sample - loss: 2.5066e-05 - val_loss: 1.4087e-05
Epoch 73/1000
3888/3888 [==============================] - 2s 522us/sample - loss: 3.0623e-05 - val_loss: 2.6909e-05
Epoch 74/1000
3888/3888 [==============================] - 2s 510us/sample - loss: 2.5317e-05 - val_loss: 1.0060e-05
Epoch 75/1000
3888/3888 [==============================] - 2s 518us/sample - loss: 6.0347e-05 - val_loss: 1.3934e-05
Epoch 76/1000
3888/3888 [==============================] - 2s 517us/sample - loss: 1.4041e-05 - val_loss: 1.2806e-05
Epoch 77/1000
3888/3888 [==============================] - 2s 520us/sample - loss: 2.6030e-05 - val_loss: 1.6550e-05
Epoch 78/1000
3888/3888 [==============================] - 2s 522us/sample - loss: 3.3747e-05 - val_loss: 9.9822e-06
Epoch 79/1000
3888/3888 [==============================] - 2s 521us/sample - loss: 1.7553e-05 - val_loss: 1.7042e-04
Epoch 80/1000
3888/3888 [==============================] - 2s 516us/sample - loss: 4.5654e-05 - val_loss: 2.2506e-05
Epoch 81/1000
3888/3888 [==============================] - 2s 512us/sample - loss: 1.9163e-05 - val_loss: 1.1224e-05
Epoch 82/1000
3888/3888 [==============================] - 2s 520us/sample - loss: 1.6265e-05 - val_loss: 1.7619e-05
Epoch 83/1000
3888/3888 [==============================] - 2s 515us/sample - loss: 4.3717e-05 - val_loss: 1.4063e-05
Epoch 84/1000
3888/3888 [==============================] - 2s 526us/sample - loss: 3.5289e-05 - val_loss: 9.6553e-06
Epoch 85/1000
3888/3888 [==============================] - 2s 526us/sample - loss: 1.4116e-05 - val_loss: 2.7602e-05
Epoch 86/1000
3888/3888 [==============================] - 2s 514us/sample - loss: 2.5642e-05 - val_loss: 6.0762e-05
Epoch 87/1000
3888/3888 [==============================] - 2s 522us/sample - loss: 3.9845e-05 - val_loss: 1.5329e-05
Epoch 88/1000
3888/3888 [==============================] - 2s 514us/sample - loss: 2.6384e-05 - val_loss: 2.9309e-04
Epoch 89/1000
3888/3888 [==============================] - 2s 529us/sample - loss: 2.0632e-05 - val_loss: 2.0136e-05
Epoch 90/1000
3888/3888 [==============================] - 2s 514us/sample - loss: 1.4065e-05 - val_loss: 1.8223e-05
Epoch 91/1000
3888/3888 [==============================] - 2s 515us/sample - loss: 3.5978e-05 - val_loss: 1.1969e-05
Epoch 92/1000
3888/3888 [==============================] - 2s 524us/sample - loss: 2.0140e-05 - val_loss: 1.3532e-05
Epoch 93/1000
3888/3888 [==============================] - 2s 521us/sample - loss: 1.4949e-05 - val_loss: 7.4074e-06
Epoch 94/1000
3888/3888 [==============================] - 2s 516us/sample - loss: 1.6803e-05 - val_loss: 1.9807e-05
Epoch 95/1000
3888/3888 [==============================] - 2s 541us/sample - loss: 4.0814e-05 - val_loss: 2.1695e-05
Epoch 96/1000
3888/3888 [==============================] - 2s 520us/sample - loss: 1.1174e-05 - val_loss: 1.5759e-05
Epoch 97/1000
3888/3888 [==============================] - 2s 528us/sample - loss: 1.7318e-05 - val_loss: 3.7607e-05
Epoch 98/1000
3888/3888 [==============================] - 2s 517us/sample - loss: 1.9572e-05 - val_loss: 1.1790e-05
Epoch 99/1000
3888/3888 [==============================] - 2s 517us/sample - loss: 3.9336e-05 - val_loss: 3.8419e-05
Epoch 100/1000
3888/3888 [==============================] - 2s 510us/sample - loss: 1.4113e-05 - val_loss: 1.1696e-05
Epoch 101/1000
3888/3888 [==============================] - 2s 516us/sample - loss: 4.7618e-05 - val_loss: 2.4654e-05
Epoch 102/1000
3888/3888 [==============================] - 2s 534us/sample - loss: 1.0097e-05 - val_loss: 8.5651e-06
Epoch 103/1000
3888/3888 [==============================] - 2s 525us/sample - loss: 8.5207e-06 - val_loss: 4.1195e-05
Epoch 104/1000
3888/3888 [==============================] - 2s 510us/sample - loss: 1.9288e-05 - val_loss: 3.5934e-05
Epoch 105/1000
3888/3888 [==============================] - 2s 519us/sample - loss: 1.4522e-05 - val_loss: 4.7873e-05
Epoch 106/1000
3888/3888 [==============================] - 2s 510us/sample - loss: 2.8465e-05 - val_loss: 8.8930e-06
Epoch 107/1000
3888/3888 [==============================] - 2s 519us/sample - loss: 1.2106e-05 - val_loss: 8.2797e-06
Epoch 108/1000
3888/3888 [==============================] - 2s 506us/sample - loss: 2.1212e-05 - val_loss: 1.3833e-05
Epoch 109/1000
3888/3888 [==============================] - 2s 519us/sample - loss: 3.1905e-05 - val_loss: 7.8618e-06
Epoch 110/1000
3888/3888 [==============================] - 2s 515us/sample - loss: 1.2219e-05 - val_loss: 1.3177e-05
Epoch 111/1000
3888/3888 [==============================] - 2s 511us/sample - loss: 3.6925e-05 - val_loss: 8.0115e-06
Epoch 112/1000
3888/3888 [==============================] - 2s 529us/sample - loss: 6.7165e-06 - val_loss: 8.4891e-06
Epoch 113/1000
3888/3888 [==============================] - 2s 515us/sample - loss: 1.1984e-05 - val_loss: 2.1423e-05
Epoch 114/1000
3888/3888 [==============================] - 2s 520us/sample - loss: 1.8656e-05 - val_loss: 1.8823e-05
Epoch 115/1000
3888/3888 [==============================] - 2s 520us/sample - loss: 1.1904e-05 - val_loss: 8.2761e-06
Epoch 116/1000
3888/3888 [==============================] - 2s 517us/sample - loss: 4.0860e-05 - val_loss: 5.4605e-05
Epoch 117/1000
3888/3888 [==============================] - 2s 515us/sample - loss: 8.4449e-06 - val_loss: 5.1097e-06
Epoch 118/1000
3888/3888 [==============================] - 2s 506us/sample - loss: 7.8520e-06 - val_loss: 2.2092e-05
Epoch 119/1000
3888/3888 [==============================] - 2s 516us/sample - loss: 2.5702e-05 - val_loss: 4.5121e-05
Epoch 120/1000
3888/3888 [==============================] - 2s 514us/sample - loss: 8.6415e-06 - val_loss: 7.5840e-06
Epoch 121/1000
3888/3888 [==============================] - 2s 517us/sample - loss: 1.3241e-05 - val_loss: 5.7292e-04
Epoch 122/1000
3888/3888 [==============================] - 2s 513us/sample - loss: 2.6270e-05 - val_loss: 6.8134e-06
Epoch 123/1000
3888/3888 [==============================] - 2s 523us/sample - loss: 8.2742e-06 - val_loss: 1.1918e-05
Epoch 124/1000
3888/3888 [==============================] - 2s 524us/sample - loss: 1.3204e-05 - val_loss: 9.0442e-06
Epoch 125/1000
3888/3888 [==============================] - 2s 527us/sample - loss: 1.7324e-05 - val_loss: 7.0954e-06
Epoch 126/1000
3888/3888 [==============================] - 2s 521us/sample - loss: 1.4779e-05 - val_loss: 5.9253e-06
Epoch 127/1000
3888/3888 [==============================] - 2s 524us/sample - loss: 2.4810e-05 - val_loss: 9.5395e-05
Epoch 128/1000
3888/3888 [==============================] - 2s 518us/sample - loss: 1.5551e-05 - val_loss: 7.6527e-06
Epoch 129/1000
3888/3888 [==============================] - 2s 532us/sample - loss: 2.2809e-05 - val_loss: 7.3738e-06
Epoch 130/1000
3888/3888 [==============================] - 2s 513us/sample - loss: 5.9714e-06 - val_loss: 8.6370e-06
Epoch 131/1000
3888/3888 [==============================] - 2s 536us/sample - loss: 2.4162e-05 - val_loss: 1.0039e-05
Epoch 132/1000
3888/3888 [==============================] - 2s 529us/sample - loss: 9.6565e-06 - val_loss: 6.6872e-06
Epoch 133/1000
3888/3888 [==============================] - 2s 523us/sample - loss: 1.0361e-05 - val_loss: 8.5798e-05
Epoch 134/1000
3888/3888 [==============================] - 2s 516us/sample - loss: 2.2593e-05 - val_loss: 5.7562e-06
Epoch 135/1000
3888/3888 [==============================] - 2s 522us/sample - loss: 5.7494e-06 - val_loss: 1.4141e-05
Epoch 136/1000
3888/3888 [==============================] - 2s 512us/sample - loss: 3.5733e-05 - val_loss: 3.2080e-05
Epoch 137/1000
3888/3888 [==============================] - 2s 519us/sample - loss: 6.8600e-06 - val_loss: 7.5116e-06
Epoch 138/1000
3888/3888 [==============================] - 2s 524us/sample - loss: 1.2544e-05 - val_loss: 1.3008e-05
Epoch 139/1000
3888/3888 [==============================] - 2s 521us/sample - loss: 1.5438e-05 - val_loss: 7.3079e-06
Epoch 140/1000
3888/3888 [==============================] - 2s 515us/sample - loss: 1.4497e-05 - val_loss: 1.0710e-05
Epoch 141/1000
3888/3888 [==============================] - 2s 516us/sample - loss: 8.9607e-06 - val_loss: 2.4513e-05
Epoch 142/1000
3888/3888 [==============================] - 2s 517us/sample - loss: 8.4252e-06 - val_loss: 5.6528e-06
Epoch 143/1000
3888/3888 [==============================] - 2s 521us/sample - loss: 1.5146e-05 - val_loss: 5.6560e-06
Epoch 144/1000
3888/3888 [==============================] - 2s 519us/sample - loss: 1.2827e-05 - val_loss: 2.4622e-05
Epoch 145/1000
3888/3888 [==============================] - 2s 520us/sample - loss: 2.6252e-05 - val_loss: 7.7830e-06
Epoch 146/1000
3888/3888 [==============================] - 2s 523us/sample - loss: 9.8901e-06 - val_loss: 7.7425e-06
Epoch 147/1000
3888/3888 [==============================] - 2s 515us/sample - loss: 1.0156e-05 - val_loss: 6.5091e-06
Epoch 148/1000
3888/3888 [==============================] - 2s 520us/sample - loss: 2.2889e-05 - val_loss: 7.4927e-06
Epoch 149/1000
3888/3888 [==============================] - 2s 535us/sample - loss: 1.2111e-05 - val_loss: 5.6396e-06
Epoch 150/1000
3888/3888 [==============================] - 2s 522us/sample - loss: 1.1303e-05 - val_loss: 1.0146e-05
Epoch 151/1000
3888/3888 [==============================] - 2s 526us/sample - loss: 1.1524e-05 - val_loss: 1.0757e-04
Epoch 152/1000
3888/3888 [==============================] - 2s 522us/sample - loss: 1.2155e-05 - val_loss: 7.1149e-06
Epoch 153/1000
3888/3888 [==============================] - 2s 530us/sample - loss: 1.9844e-05 - val_loss: 4.1217e-06
Epoch 154/1000
3888/3888 [==============================] - 2s 516us/sample - loss: 7.2853e-06 - val_loss: 2.3351e-05
Epoch 155/1000
3888/3888 [==============================] - 2s 523us/sample - loss: 1.3743e-05 - val_loss: 5.8358e-06
Epoch 156/1000
3888/3888 [==============================] - 2s 519us/sample - loss: 1.1266e-05 - val_loss: 9.5893e-05
Epoch 157/1000
3888/3888 [==============================] - 2s 537us/sample - loss: 3.9770e-05 - val_loss: 4.6877e-06
Epoch 158/1000
3888/3888 [==============================] - 2s 523us/sample - loss: 4.2406e-06 - val_loss: 4.8267e-06
Epoch 159/1000
3888/3888 [==============================] - 2s 527us/sample - loss: 6.5291e-06 - val_loss: 7.1137e-06
Epoch 160/1000
3888/3888 [==============================] - 2s 527us/sample - loss: 5.1563e-06 - val_loss: 1.2186e-05
Epoch 161/1000
3888/3888 [==============================] - 2s 532us/sample - loss: 1.1969e-05 - val_loss: 1.5704e-05
Epoch 162/1000
3888/3888 [==============================] - 2s 512us/sample - loss: 1.1371e-05 - val_loss: 7.0800e-06
Epoch 163/1000
3888/3888 [==============================] - 2s 517us/sample - loss: 1.1869e-05 - val_loss: 5.0365e-06
Epoch 164/1000
3888/3888 [==============================] - 2s 532us/sample - loss: 1.7494e-05 - val_loss: 5.4833e-06
Epoch 165/1000
3888/3888 [==============================] - 2s 513us/sample - loss: 8.1931e-06 - val_loss: 1.6637e-05
Epoch 166/1000
3888/3888 [==============================] - 2s 526us/sample - loss: 4.8346e-05 - val_loss: 4.1029e-06
Epoch 167/1000
3888/3888 [==============================] - 2s 518us/sample - loss: 3.8181e-06 - val_loss: 4.1837e-06
Epoch 168/1000
3888/3888 [==============================] - 2s 516us/sample - loss: 4.1776e-06 - val_loss: 4.5184e-06
Epoch 169/1000
3888/3888 [==============================] - 2s 521us/sample - loss: 6.1066e-06 - val_loss: 4.4452e-06
Epoch 170/1000
3888/3888 [==============================] - 2s 521us/sample - loss: 7.8181e-06 - val_loss: 5.5000e-06
Epoch 171/1000
3888/3888 [==============================] - 2s 524us/sample - loss: 1.0267e-05 - val_loss: 6.7189e-06
Epoch 172/1000
3888/3888 [==============================] - 2s 511us/sample - loss: 8.2655e-06 - val_loss: 6.9044e-05
Epoch 173/1000
3888/3888 [==============================] - 2s 505us/sample - loss: 1.1067e-05 - val_loss: 1.3984e-05
Epoch 174/1000
3888/3888 [==============================] - 2s 512us/sample - loss: 2.6084e-05 - val_loss: 6.4030e-06
Epoch 175/1000
3888/3888 [==============================] - 2s 512us/sample - loss: 5.6664e-06 - val_loss: 1.0517e-05
Epoch 176/1000
3888/3888 [==============================] - 2s 514us/sample - loss: 4.3920e-06 - val_loss: 1.0428e-05
Epoch 177/1000
3888/3888 [==============================] - 2s 509us/sample - loss: 2.3571e-05 - val_loss: 5.7982e-06
Epoch 178/1000
3888/3888 [==============================] - 2s 518us/sample - loss: 4.8672e-06 - val_loss: 8.1345e-06
Epoch 179/1000
3888/3888 [==============================] - 2s 512us/sample - loss: 1.0596e-05 - val_loss: 1.8635e-05
Epoch 180/1000
3888/3888 [==============================] - 2s 522us/sample - loss: 1.0320e-05 - val_loss: 3.8052e-06
Epoch 181/1000
3888/3888 [==============================] - 2s 519us/sample - loss: 9.6965e-06 - val_loss: 7.7871e-06
Epoch 182/1000
3888/3888 [==============================] - 2s 521us/sample - loss: 1.3212e-05 - val_loss: 8.9347e-06
Epoch 183/1000
3888/3888 [==============================] - 2s 520us/sample - loss: 5.3752e-06 - val_loss: 1.2881e-05
Epoch 184/1000
3888/3888 [==============================] - 2s 517us/sample - loss: 1.0595e-05 - val_loss: 3.3365e-06
Epoch 185/1000
3888/3888 [==============================] - 2s 529us/sample - loss: 1.3169e-05 - val_loss: 7.2238e-06
Epoch 186/1000
3888/3888 [==============================] - 2s 520us/sample - loss: 9.8885e-06 - val_loss: 9.1990e-06
Epoch 187/1000
3888/3888 [==============================] - 2s 527us/sample - loss: 1.5256e-05 - val_loss: 5.4908e-06
Epoch 188/1000
3888/3888 [==============================] - 2s 525us/sample - loss: 1.6004e-05 - val_loss: 4.8164e-06
Epoch 189/1000
3888/3888 [==============================] - 2s 516us/sample - loss: 4.9121e-06 - val_loss: 6.4997e-06
Epoch 190/1000
3888/3888 [==============================] - 2s 524us/sample - loss: 8.0949e-06 - val_loss: 4.1455e-05
Epoch 191/1000
3888/3888 [==============================] - 2s 520us/sample - loss: 9.4887e-06 - val_loss: 6.6267e-06
Epoch 192/1000
3888/3888 [==============================] - 2s 526us/sample - loss: 1.1911e-05 - val_loss: 7.8412e-06
Epoch 193/1000
3888/3888 [==============================] - 2s 521us/sample - loss: 2.2724e-05 - val_loss: 3.9957e-06
Epoch 194/1000
3888/3888 [==============================] - 2s 513us/sample - loss: 4.1641e-06 - val_loss: 3.6641e-06
Epoch 195/1000
3888/3888 [==============================] - 2s 517us/sample - loss: 1.6077e-05 - val_loss: 4.9613e-06
Epoch 196/1000
3888/3888 [==============================] - 2s 533us/sample - loss: 4.1301e-06 - val_loss: 3.1753e-06
Epoch 197/1000
3888/3888 [==============================] - 2s 527us/sample - loss: 1.3887e-05 - val_loss: 4.2216e-06
Epoch 198/1000
3888/3888 [==============================] - 2s 515us/sample - loss: 4.2333e-06 - val_loss: 9.9052e-05
Epoch 199/1000
3888/3888 [==============================] - 2s 517us/sample - loss: 1.7420e-05 - val_loss: 6.1529e-06
Epoch 200/1000
3888/3888 [==============================] - 2s 521us/sample - loss: 6.3075e-06 - val_loss: 6.4819e-06
Epoch 201/1000
3888/3888 [==============================] - 2s 507us/sample - loss: 6.9023e-06 - val_loss: 1.9570e-05
Epoch 202/1000
3888/3888 [==============================] - 2s 520us/sample - loss: 1.2040e-05 - val_loss: 5.9059e-06
Epoch 203/1000
3888/3888 [==============================] - 2s 515us/sample - loss: 3.1139e-05 - val_loss: 8.3588e-06
Epoch 204/1000
3888/3888 [==============================] - 2s 531us/sample - loss: 3.9011e-06 - val_loss: 3.5444e-06
Epoch 205/1000
3888/3888 [==============================] - 2s 513us/sample - loss: 3.1178e-06 - val_loss: 3.4576e-06
Epoch 206/1000
3888/3888 [==============================] - 2s 516us/sample - loss: 9.0759e-06 - val_loss: 3.6263e-06
Epoch 207/1000
3888/3888 [==============================] - 2s 515us/sample - loss: 7.6604e-06 - val_loss: 9.3612e-06
Epoch 208/1000
3888/3888 [==============================] - 2s 526us/sample - loss: 7.4209e-06 - val_loss: 7.7182e-06
Epoch 209/1000
3888/3888 [==============================] - 2s 520us/sample - loss: 7.0581e-06 - val_loss: 1.0622e-04
Epoch 210/1000
3888/3888 [==============================] - 2s 529us/sample - loss: 1.1246e-05 - val_loss: 7.2725e-06
Epoch 211/1000
3888/3888 [==============================] - 2s 524us/sample - loss: 1.1250e-05 - val_loss: 8.1946e-05
Epoch 212/1000
3888/3888 [==============================] - 2s 522us/sample - loss: 1.2572e-05 - val_loss: 4.7253e-06
Epoch 213/1000
3888/3888 [==============================] - 2s 517us/sample - loss: 7.2925e-06 - val_loss: 4.5228e-06
Epoch 214/1000
3888/3888 [==============================] - 2s 524us/sample - loss: 1.3443e-05 - val_loss: 3.5181e-06
Epoch 215/1000
3888/3888 [==============================] - 2s 523us/sample - loss: 4.9517e-06 - val_loss: 1.3186e-05
Epoch 216/1000
3888/3888 [==============================] - 2s 525us/sample - loss: 1.9197e-05 - val_loss: 4.1599e-06
Epoch 217/1000
3888/3888 [==============================] - 2s 526us/sample - loss: 4.9010e-06 - val_loss: 5.7298e-06
Epoch 218/1000
3888/3888 [==============================] - 2s 513us/sample - loss: 6.2516e-06 - val_loss: 5.1742e-06
Epoch 219/1000
3888/3888 [==============================] - 2s 520us/sample - loss: 6.0227e-05 - val_loss: 6.2305e-05
Epoch 220/1000
3888/3888 [==============================] - 2s 523us/sample - loss: 8.5514e-06 - val_loss: 8.4053e-06
Epoch 221/1000
3888/3888 [==============================] - 2s 524us/sample - loss: 2.9974e-06 - val_loss: 3.1485e-06
Epoch 222/1000
3888/3888 [==============================] - 2s 532us/sample - loss: 8.2789e-06 - val_loss: 2.7642e-06
Epoch 223/1000
3888/3888 [==============================] - 2s 514us/sample - loss: 5.3145e-06 - val_loss: 1.4580e-05
Epoch 224/1000
3888/3888 [==============================] - 2s 521us/sample - loss: 4.6759e-06 - val_loss: 4.4093e-06
Epoch 225/1000
3888/3888 [==============================] - 2s 523us/sample - loss: 2.8738e-05 - val_loss: 2.3206e-05
Epoch 226/1000
3888/3888 [==============================] - 2s 534us/sample - loss: 3.8442e-06 - val_loss: 3.3573e-06
Epoch 227/1000
3888/3888 [==============================] - 2s 513us/sample - loss: 3.5618e-06 - val_loss: 2.7529e-06
Epoch 228/1000
3888/3888 [==============================] - 2s 520us/sample - loss: 3.3082e-06 - val_loss: 4.1051e-06
Epoch 229/1000
3888/3888 [==============================] - 2s 523us/sample - loss: 7.8566e-06 - val_loss: 2.0916e-05
Epoch 230/1000
3888/3888 [==============================] - 2s 520us/sample - loss: 1.5488e-05 - val_loss: 3.9161e-06
Epoch 231/1000
3888/3888 [==============================] - 2s 514us/sample - loss: 6.2693e-06 - val_loss: 1.0627e-05
Epoch 232/1000
3888/3888 [==============================] - 2s 522us/sample - loss: 5.1284e-06 - val_loss: 1.1321e-05
Epoch 233/1000
3888/3888 [==============================] - 2s 536us/sample - loss: 8.6836e-06 - val_loss: 3.2489e-05
Epoch 234/1000
3888/3888 [==============================] - 2s 525us/sample - loss: 1.1587e-05 - val_loss: 2.7507e-06
Epoch 235/1000
3888/3888 [==============================] - 2s 515us/sample - loss: 6.7578e-06 - val_loss: 5.5140e-06
Epoch 236/1000
3888/3888 [==============================] - 2s 519us/sample - loss: 7.0778e-06 - val_loss: 2.3809e-05
Epoch 237/1000
3888/3888 [==============================] - 2s 522us/sample - loss: 1.3034e-05 - val_loss: 3.9254e-06
Epoch 238/1000
3888/3888 [==============================] - 2s 511us/sample - loss: 7.1849e-06 - val_loss: 4.3003e-06
Epoch 239/1000
3888/3888 [==============================] - 2s 518us/sample - loss: 8.5896e-06 - val_loss: 1.2718e-05
Epoch 240/1000
3888/3888 [==============================] - 2s 515us/sample - loss: 1.3826e-05 - val_loss: 2.8758e-06
Epoch 241/1000
3888/3888 [==============================] - 2s 515us/sample - loss: 4.1601e-06 - val_loss: 3.8652e-06
Epoch 242/1000
3888/3888 [==============================] - 2s 525us/sample - loss: 6.1050e-06 - val_loss: 1.1409e-05
Epoch 243/1000
3888/3888 [==============================] - 2s 514us/sample - loss: 8.8223e-06 - val_loss: 4.1144e-06
Epoch 244/1000
3888/3888 [==============================] - 2s 520us/sample - loss: 8.8962e-06 - val_loss: 3.4990e-06
Epoch 245/1000
3888/3888 [==============================] - 2s 523us/sample - loss: 1.4779e-05 - val_loss: 4.1267e-06
Epoch 246/1000
3888/3888 [==============================] - 2s 511us/sample - loss: 5.9276e-06 - val_loss: 7.9941e-06
Epoch 247/1000
3888/3888 [==============================] - 2s 507us/sample - loss: 7.0969e-06 - val_loss: 2.7558e-06
Epoch 248/1000
3888/3888 [==============================] - 2s 524us/sample - loss: 2.2623e-05 - val_loss: 5.4240e-06
Epoch 249/1000
3888/3888 [==============================] - 2s 515us/sample - loss: 3.0067e-06 - val_loss: 2.3855e-05
Epoch 250/1000
3888/3888 [==============================] - 2s 517us/sample - loss: 1.3376e-05 - val_loss: 4.7684e-06
Epoch 251/1000
3888/3888 [==============================] - 2s 519us/sample - loss: 3.1349e-06 - val_loss: 2.7506e-06
Epoch 252/1000
3888/3888 [==============================] - 2s 519us/sample - loss: 3.0233e-06 - val_loss: 2.7614e-06
Epoch 253/1000
3888/3888 [==============================] - 2s 514us/sample - loss: 5.4205e-06 - val_loss: 5.4551e-06
Epoch 254/1000
3888/3888 [==============================] - 2s 514us/sample - loss: 9.0579e-06 - val_loss: 1.3623e-04
Epoch 255/1000
3888/3888 [==============================] - 2s 509us/sample - loss: 1.1037e-05 - val_loss: 3.1331e-06
Epoch 256/1000
3888/3888 [==============================] - 2s 523us/sample - loss: 5.8418e-06 - val_loss: 1.3900e-05
Epoch 257/1000
3888/3888 [==============================] - 2s 520us/sample - loss: 2.0149e-05 - val_loss: 5.6909e-06
Epoch 258/1000
3888/3888 [==============================] - 2s 513us/sample - loss: 3.1772e-06 - val_loss: 3.9846e-06
Epoch 259/1000
3888/3888 [==============================] - 2s 509us/sample - loss: 3.8305e-06 - val_loss: 6.8447e-06
Epoch 260/1000
3888/3888 [==============================] - 2s 504us/sample - loss: 9.0661e-06 - val_loss: 3.3400e-06
Epoch 261/1000
3888/3888 [==============================] - 2s 520us/sample - loss: 6.0070e-06 - val_loss: 5.0809e-06
Epoch 262/1000
3888/3888 [==============================] - 2s 521us/sample - loss: 5.7080e-06 - val_loss: 4.8959e-06
Epoch 263/1000
3888/3888 [==============================] - 2s 523us/sample - loss: 9.4947e-06 - val_loss: 5.6802e-06
Epoch 264/1000
3888/3888 [==============================] - 2s 530us/sample - loss: 1.1174e-05 - val_loss: 5.8145e-06
Epoch 265/1000
3888/3888 [==============================] - 2s 516us/sample - loss: 9.3153e-06 - val_loss: 5.9804e-06
Epoch 266/1000
3888/3888 [==============================] - 2s 513us/sample - loss: 6.6430e-06 - val_loss: 2.1847e-06
Epoch 267/1000
3888/3888 [==============================] - 2s 518us/sample - loss: 8.5639e-06 - val_loss: 6.0531e-06
Epoch 268/1000
3888/3888 [==============================] - 2s 507us/sample - loss: 1.6147e-05 - val_loss: 3.4445e-05
Epoch 269/1000
3888/3888 [==============================] - 2s 519us/sample - loss: 4.4172e-06 - val_loss: 3.6505e-06
Epoch 270/1000
3888/3888 [==============================] - 2s 516us/sample - loss: 3.5948e-06 - val_loss: 5.2941e-06
Epoch 271/1000
3888/3888 [==============================] - 2s 530us/sample - loss: 1.9204e-05 - val_loss: 4.1708e-06
Epoch 272/1000
3888/3888 [==============================] - 2s 526us/sample - loss: 3.0636e-06 - val_loss: 2.8033e-06
Epoch 273/1000
3888/3888 [==============================] - 2s 528us/sample - loss: 3.7931e-06 - val_loss: 1.1575e-05
Epoch 274/1000
3888/3888 [==============================] - 2s 520us/sample - loss: 1.0040e-05 - val_loss: 5.1195e-06
Epoch 275/1000
3888/3888 [==============================] - 2s 518us/sample - loss: 1.0341e-05 - val_loss: 3.9013e-06
Epoch 276/1000
3888/3888 [==============================] - 2s 508us/sample - loss: 1.3690e-05 - val_loss: 2.3333e-05
Epoch 277/1000
3888/3888 [==============================] - 2s 523us/sample - loss: 4.2282e-06 - val_loss: 2.8866e-06
Epoch 278/1000
3888/3888 [==============================] - 2s 524us/sample - loss: 3.8540e-06 - val_loss: 2.2839e-06
Epoch 279/1000
3888/3888 [==============================] - 2s 523us/sample - loss: 6.1632e-06 - val_loss: 6.5055e-06
Epoch 280/1000
3888/3888 [==============================] - 2s 517us/sample - loss: 1.2697e-05 - val_loss: 9.6706e-06
Epoch 281/1000
3888/3888 [==============================] - 2s 514us/sample - loss: 5.0937e-06 - val_loss: 4.4150e-06
Epoch 282/1000
3888/3888 [==============================] - 2s 526us/sample - loss: 9.1255e-06 - val_loss: 2.6147e-06
Epoch 283/1000
3888/3888 [==============================] - 2s 525us/sample - loss: 1.1346e-05 - val_loss: 1.4970e-05
Epoch 284/1000
3888/3888 [==============================] - 2s 519us/sample - loss: 1.9381e-05 - val_loss: 2.8766e-06
Epoch 285/1000
3888/3888 [==============================] - 2s 512us/sample - loss: 2.3445e-06 - val_loss: 2.3841e-06
Epoch 286/1000
3888/3888 [==============================] - 2s 509us/sample - loss: 2.9023e-06 - val_loss: 5.4774e-06
Epoch 287/1000
3888/3888 [==============================] - 2s 513us/sample - loss: 5.9073e-06 - val_loss: 2.5260e-06
Epoch 288/1000
3888/3888 [==============================] - 2s 517us/sample - loss: 5.0261e-06 - val_loss: 4.8703e-06
Epoch 289/1000
3888/3888 [==============================] - 2s 518us/sample - loss: 8.6048e-06 - val_loss: 2.0882e-05
Epoch 290/1000
3888/3888 [==============================] - 2s 523us/sample - loss: 8.0509e-06 - val_loss: 5.8226e-06
Epoch 291/1000
3888/3888 [==============================] - 2s 511us/sample - loss: 5.8337e-06 - val_loss: 3.3843e-06
Epoch 292/1000
3888/3888 [==============================] - 2s 515us/sample - loss: 8.6691e-06 - val_loss: 1.2504e-05
Epoch 293/1000
3888/3888 [==============================] - 2s 517us/sample - loss: 9.5804e-06 - val_loss: 1.2868e-05
Epoch 294/1000
3888/3888 [==============================] - 2s 526us/sample - loss: 8.6848e-06 - val_loss: 6.9553e-06
Epoch 295/1000
3888/3888 [==============================] - 2s 524us/sample - loss: 9.7696e-06 - val_loss: 9.8989e-06
Epoch 296/1000
3888/3888 [==============================] - 2s 530us/sample - loss: 1.1448e-05 - val_loss: 1.0150e-05
Epoch 297/1000
3888/3888 [==============================] - 2s 523us/sample - loss: 4.8309e-06 - val_loss: 3.2402e-06
Epoch 298/1000
3888/3888 [==============================] - 2s 513us/sample - loss: 6.0955e-06 - val_loss: 4.1831e-06
Epoch 299/1000
3888/3888 [==============================] - 2s 518us/sample - loss: 2.0048e-05 - val_loss: 6.8533e-06
Epoch 300/1000
3888/3888 [==============================] - 2s 524us/sample - loss: 2.5143e-06 - val_loss: 1.8474e-06
Epoch 301/1000
3888/3888 [==============================] - 2s 527us/sample - loss: 7.3177e-06 - val_loss: 1.1143e-05
Epoch 302/1000
3888/3888 [==============================] - 2s 521us/sample - loss: 4.9378e-06 - val_loss: 5.8322e-06
Epoch 303/1000
3888/3888 [==============================] - 2s 529us/sample - loss: 4.2476e-06 - val_loss: 2.5494e-05
Epoch 304/1000
3888/3888 [==============================] - 2s 540us/sample - loss: 1.4943e-05 - val_loss: 4.1072e-06
Epoch 305/1000
3888/3888 [==============================] - 2s 534us/sample - loss: 3.2359e-06 - val_loss: 2.9837e-06
Epoch 306/1000
3888/3888 [==============================] - 2s 518us/sample - loss: 4.7132e-06 - val_loss: 7.2627e-06
Epoch 307/1000
3888/3888 [==============================] - 2s 530us/sample - loss: 5.1347e-06 - val_loss: 2.4915e-06
Epoch 308/1000
3888/3888 [==============================] - 2s 524us/sample - loss: 1.0642e-05 - val_loss: 5.9635e-06
Epoch 309/1000
3888/3888 [==============================] - 2s 535us/sample - loss: 2.2651e-05 - val_loss: 4.8468e-06
Epoch 310/1000
3888/3888 [==============================] - 2s 533us/sample - loss: 2.1439e-06 - val_loss: 2.5291e-06
Epoch 311/1000
3888/3888 [==============================] - 2s 518us/sample - loss: 1.0768e-05 - val_loss: 4.4883e-06
Epoch 312/1000
3888/3888 [==============================] - 2s 515us/sample - loss: 2.3849e-06 - val_loss: 3.5999e-06
Epoch 313/1000
3888/3888 [==============================] - 2s 514us/sample - loss: 5.8903e-06 - val_loss: 3.3549e-06
Epoch 314/1000
3888/3888 [==============================] - 2s 514us/sample - loss: 4.0148e-06 - val_loss: 4.0536e-06
Epoch 315/1000
3888/3888 [==============================] - 2s 533us/sample - loss: 7.4665e-06 - val_loss: 4.3147e-05
Epoch 316/1000
3888/3888 [==============================] - 2s 524us/sample - loss: 8.9976e-06 - val_loss: 2.5554e-06
Epoch 317/1000
3888/3888 [==============================] - 2s 524us/sample - loss: 6.1528e-06 - val_loss: 2.2465e-06
Epoch 318/1000
3888/3888 [==============================] - 2s 532us/sample - loss: 5.4038e-06 - val_loss: 4.1070e-06
Epoch 319/1000
3888/3888 [==============================] - 2s 543us/sample - loss: 4.5286e-06 - val_loss: 5.4174e-06
Epoch 320/1000
3888/3888 [==============================] - 2s 522us/sample - loss: 1.3519e-05 - val_loss: 2.1794e-06
Epoch 321/1000
3888/3888 [==============================] - 2s 529us/sample - loss: 3.5707e-06 - val_loss: 3.5886e-06
Epoch 322/1000
3888/3888 [==============================] - 2s 526us/sample - loss: 8.5194e-06 - val_loss: 8.8490e-06
Epoch 323/1000
3888/3888 [==============================] - 2s 527us/sample - loss: 7.8801e-06 - val_loss: 6.4011e-05
Epoch 324/1000
3888/3888 [==============================] - 2s 529us/sample - loss: 9.5432e-06 - val_loss: 5.1333e-06
Epoch 325/1000
3888/3888 [==============================] - 2s 527us/sample - loss: 4.6362e-06 - val_loss: 3.8785e-06
Epoch 326/1000
3888/3888 [==============================] - 2s 513us/sample - loss: 7.1403e-06 - val_loss: 2.4805e-06
Epoch 327/1000
3888/3888 [==============================] - 2s 510us/sample - loss: 6.4191e-06 - val_loss: 3.9879e-06
Epoch 328/1000
3888/3888 [==============================] - 2s 530us/sample - loss: 6.3707e-06 - val_loss: 2.0556e-05
Epoch 329/1000
3888/3888 [==============================] - 2s 516us/sample - loss: 7.7769e-06 - val_loss: 2.5108e-05
Epoch 330/1000
3888/3888 [==============================] - 2s 521us/sample - loss: 8.6417e-06 - val_loss: 2.8069e-06
Epoch 331/1000
3888/3888 [==============================] - 2s 526us/sample - loss: 5.6465e-06 - val_loss: 1.1017e-05
Epoch 332/1000
3888/3888 [==============================] - 2s 513us/sample - loss: 6.1531e-06 - val_loss: 3.1217e-06
Epoch 333/1000
3888/3888 [==============================] - 2s 521us/sample - loss: 5.7912e-06 - val_loss: 7.2552e-06
Epoch 334/1000
3888/3888 [==============================] - 2s 520us/sample - loss: 8.9585e-06 - val_loss: 1.3419e-05
Epoch 335/1000
3888/3888 [==============================] - 2s 519us/sample - loss: 8.7274e-06 - val_loss: 0.0014
Epoch 336/1000
3888/3888 [==============================] - 2s 518us/sample - loss: 3.7067e-05 - val_loss: 1.8333e-06
Epoch 337/1000
3888/3888 [==============================] - 2s 521us/sample - loss: 1.8976e-06 - val_loss: 2.1979e-06
Epoch 338/1000
3888/3888 [==============================] - 2s 517us/sample - loss: 2.0615e-06 - val_loss: 2.0269e-06
Epoch 339/1000
3888/3888 [==============================] - 2s 517us/sample - loss: 4.2314e-06 - val_loss: 8.4085e-06
Epoch 340/1000
3888/3888 [==============================] - 2s 515us/sample - loss: 3.2186e-06 - val_loss: 2.9493e-06
Epoch 341/1000
3888/3888 [==============================] - 2s 525us/sample - loss: 1.3227e-05 - val_loss: 4.1129e-06
Epoch 342/1000
3888/3888 [==============================] - 2s 521us/sample - loss: 2.8331e-06 - val_loss: 4.3980e-06
Epoch 343/1000
3888/3888 [==============================] - 2s 518us/sample - loss: 2.5330e-06 - val_loss: 4.5573e-06
Epoch 344/1000
3888/3888 [==============================] - 2s 521us/sample - loss: 8.7895e-06 - val_loss: 2.0715e-06
Epoch 345/1000
3888/3888 [==============================] - 2s 516us/sample - loss: 3.4397e-06 - val_loss: 3.1976e-06
Epoch 346/1000
3888/3888 [==============================] - 2s 518us/sample - loss: 8.6552e-06 - val_loss: 6.4671e-06
Epoch 347/1000
3888/3888 [==============================] - 2s 526us/sample - loss: 4.9543e-06 - val_loss: 2.4473e-06
Epoch 348/1000
3888/3888 [==============================] - 2s 527us/sample - loss: 8.2030e-06 - val_loss: 2.1625e-05
Epoch 349/1000
3888/3888 [==============================] - 2s 512us/sample - loss: 5.2965e-06 - val_loss: 1.3248e-05
Epoch 350/1000
3888/3888 [==============================] - 2s 531us/sample - loss: 3.5001e-06 - val_loss: 3.4564e-06
Epoch 351/1000
3888/3888 [==============================] - 2s 516us/sample - loss: 2.1563e-05 - val_loss: 2.1468e-06
Epoch 352/1000
3888/3888 [==============================] - 2s 524us/sample - loss: 4.2157e-06 - val_loss: 2.5329e-06
Epoch 353/1000
3888/3888 [==============================] - 2s 521us/sample - loss: 3.2324e-06 - val_loss: 2.3022e-06
Epoch 354/1000
3888/3888 [==============================] - 2s 518us/sample - loss: 3.0538e-06 - val_loss: 2.3155e-06
Epoch 355/1000
3888/3888 [==============================] - 2s 517us/sample - loss: 9.7924e-06 - val_loss: 1.3314e-05
Epoch 356/1000
3888/3888 [==============================] - 2s 531us/sample - loss: 5.1538e-06 - val_loss: 6.7261e-06
Epoch 357/1000
3888/3888 [==============================] - 2s 529us/sample - loss: 3.9929e-06 - val_loss: 3.2329e-06
Epoch 358/1000
3888/3888 [==============================] - 2s 541us/sample - loss: 6.0119e-06 - val_loss: 1.1234e-05
Epoch 359/1000
3888/3888 [==============================] - 2s 531us/sample - loss: 1.4028e-05 - val_loss: 5.4172e-06
Epoch 360/1000
3888/3888 [==============================] - 2s 529us/sample - loss: 4.1374e-06 - val_loss: 2.3146e-06
Epoch 361/1000
3888/3888 [==============================] - 2s 520us/sample - loss: 8.2467e-06 - val_loss: 1.4046e-05
Epoch 362/1000
3888/3888 [==============================] - 2s 527us/sample - loss: 7.3327e-06 - val_loss: 3.9584e-05
Epoch 363/1000
3888/3888 [==============================] - 2s 538us/sample - loss: 5.9798e-06 - val_loss: 2.4901e-06
Epoch 364/1000
3888/3888 [==============================] - 2s 529us/sample - loss: 1.1299e-05 - val_loss: 3.2310e-06
Epoch 365/1000
3888/3888 [==============================] - 2s 507us/sample - loss: 2.5684e-06 - val_loss: 1.9659e-06
Epoch 366/1000
3888/3888 [==============================] - 2s 523us/sample - loss: 3.7174e-06 - val_loss: 2.2971e-06
Epoch 367/1000
3888/3888 [==============================] - 2s 516us/sample - loss: 2.3376e-05 - val_loss: 2.3031e-05
Epoch 368/1000
3888/3888 [==============================] - 2s 534us/sample - loss: 2.8427e-06 - val_loss: 2.1986e-06
Epoch 369/1000
3888/3888 [==============================] - 2s 526us/sample - loss: 1.0153e-05 - val_loss: 1.9796e-05
Epoch 370/1000
3888/3888 [==============================] - 2s 534us/sample - loss: 6.7771e-06 - val_loss: 3.6131e-06
Epoch 371/1000
3888/3888 [==============================] - 2s 528us/sample - loss: 1.9891e-06 - val_loss: 2.2860e-06
Epoch 372/1000
3888/3888 [==============================] - 2s 516us/sample - loss: 2.7958e-06 - val_loss: 2.4769e-06
Epoch 373/1000
3888/3888 [==============================] - 2s 516us/sample - loss: 1.1905e-05 - val_loss: 1.9560e-06
Epoch 374/1000
3888/3888 [==============================] - 2s 533us/sample - loss: 2.6675e-06 - val_loss: 2.0582e-06
Epoch 375/1000
3888/3888 [==============================] - 2s 519us/sample - loss: 2.7737e-06 - val_loss: 5.2559e-06
Epoch 376/1000
3888/3888 [==============================] - 2s 522us/sample - loss: 6.8492e-06 - val_loss: 2.9664e-06
Epoch 377/1000
3888/3888 [==============================] - 2s 523us/sample - loss: 4.7928e-06 - val_loss: 2.6107e-06
Epoch 378/1000
3888/3888 [==============================] - 2s 516us/sample - loss: 9.8566e-06 - val_loss: 1.3531e-05
Epoch 379/1000
3888/3888 [==============================] - 2s 527us/sample - loss: 5.3507e-06 - val_loss: 6.0627e-06
Epoch 380/1000
3888/3888 [==============================] - 2s 515us/sample - loss: 5.5328e-06 - val_loss: 2.2183e-05
Epoch 381/1000
3888/3888 [==============================] - 2s 524us/sample - loss: 1.0152e-05 - val_loss: 3.3916e-06
Epoch 382/1000
3888/3888 [==============================] - 2s 512us/sample - loss: 8.1791e-06 - val_loss: 2.2890e-06
Epoch 383/1000
3888/3888 [==============================] - 2s 515us/sample - loss: 1.3531e-05 - val_loss: 1.1812e-05
Epoch 384/1000
3888/3888 [==============================] - 2s 532us/sample - loss: 3.2317e-06 - val_loss: 2.0314e-06
Epoch 385/1000
3888/3888 [==============================] - 2s 520us/sample - loss: 3.5770e-06 - val_loss: 3.1171e-05
Epoch 386/1000
3888/3888 [==============================] - 2s 527us/sample - loss: 5.1324e-06 - val_loss: 6.7801e-06
Epoch 387/1000
3888/3888 [==============================] - 2s 518us/sample - loss: 9.9639e-06 - val_loss: 1.2016e-05
Epoch 388/1000
3888/3888 [==============================] - 2s 519us/sample - loss: 8.0675e-06 - val_loss: 1.9771e-06
Epoch 389/1000
3888/3888 [==============================] - 2s 518us/sample - loss: 2.4551e-06 - val_loss: 2.4244e-06
Epoch 390/1000
3888/3888 [==============================] - 2s 532us/sample - loss: 5.0200e-06 - val_loss: 3.2957e-06
Epoch 391/1000
3888/3888 [==============================] - 2s 524us/sample - loss: 4.1900e-06 - val_loss: 1.9228e-06
Epoch 392/1000
3888/3888 [==============================] - 2s 522us/sample - loss: 8.0845e-06 - val_loss: 1.9572e-05
Epoch 393/1000
3888/3888 [==============================] - 2s 516us/sample - loss: 1.3645e-05 - val_loss: 2.6554e-06
Epoch 394/1000
3888/3888 [==============================] - 2s 514us/sample - loss: 4.8637e-06 - val_loss: 2.7313e-06
Epoch 395/1000
3888/3888 [==============================] - 2s 531us/sample - loss: 2.7640e-06 - val_loss: 1.0936e-05
Epoch 396/1000
3888/3888 [==============================] - 2s 522us/sample - loss: 4.7181e-05 - val_loss: 6.4502e-05
Epoch 397/1000
3888/3888 [==============================] - 2s 514us/sample - loss: 5.2762e-06 - val_loss: 2.2628e-06
Epoch 398/1000
3888/3888 [==============================] - 2s 519us/sample - loss: 1.4881e-06 - val_loss: 1.5366e-06
Epoch 399/1000
3888/3888 [==============================] - 2s 519us/sample - loss: 1.5426e-06 - val_loss: 1.5187e-06
Epoch 400/1000
3888/3888 [==============================] - 2s 519us/sample - loss: 1.5232e-06 - val_loss: 1.5205e-06
Epoch 401/1000
3888/3888 [==============================] - 2s 525us/sample - loss: 2.0821e-06 - val_loss: 2.5434e-06
Epoch 402/1000
3888/3888 [==============================] - 2s 517us/sample - loss: 3.4016e-06 - val_loss: 2.6107e-06
Epoch 403/1000
3888/3888 [==============================] - 2s 520us/sample - loss: 5.3434e-06 - val_loss: 7.2095e-06
Epoch 404/1000
3888/3888 [==============================] - 2s 512us/sample - loss: 9.9547e-06 - val_loss: 2.9351e-06
Epoch 405/1000
3888/3888 [==============================] - 2s 520us/sample - loss: 6.2887e-06 - val_loss: 2.3486e-06
Epoch 406/1000
3888/3888 [==============================] - 2s 522us/sample - loss: 5.7482e-06 - val_loss: 2.5819e-06
Epoch 407/1000
3888/3888 [==============================] - 2s 524us/sample - loss: 2.9476e-06 - val_loss: 4.0715e-05
Epoch 408/1000
3888/3888 [==============================] - 2s 518us/sample - loss: 7.3017e-06 - val_loss: 1.4303e-06
Epoch 409/1000
3888/3888 [==============================] - 2s 514us/sample - loss: 2.6444e-06 - val_loss: 2.9202e-05
Epoch 410/1000
3888/3888 [==============================] - 2s 527us/sample - loss: 9.7810e-06 - val_loss: 2.4417e-06
Epoch 411/1000
3888/3888 [==============================] - 2s 517us/sample - loss: 2.8718e-06 - val_loss: 7.4957e-06
Epoch 412/1000
3888/3888 [==============================] - 2s 521us/sample - loss: 6.8378e-06 - val_loss: 5.9957e-06
Epoch 413/1000
3888/3888 [==============================] - 2s 525us/sample - loss: 5.3566e-06 - val_loss: 1.9532e-06
Epoch 414/1000
3888/3888 [==============================] - 2s 527us/sample - loss: 3.7647e-06 - val_loss: 2.8118e-05
Epoch 415/1000
3888/3888 [==============================] - 2s 531us/sample - loss: 6.1785e-06 - val_loss: 6.7588e-05
Epoch 416/1000
3888/3888 [==============================] - 2s 521us/sample - loss: 8.6879e-06 - val_loss: 1.5326e-05
Epoch 417/1000
3888/3888 [==============================] - 2s 526us/sample - loss: 1.3432e-05 - val_loss: 3.4263e-06
Epoch 418/1000
3888/3888 [==============================] - 2s 516us/sample - loss: 2.4245e-06 - val_loss: 2.6008e-06
Epoch 419/1000
3888/3888 [==============================] - 2s 515us/sample - loss: 2.1700e-06 - val_loss: 3.8838e-06
Epoch 420/1000
3888/3888 [==============================] - 2s 519us/sample - loss: 4.4710e-06 - val_loss: 2.4320e-06
Epoch 421/1000
3888/3888 [==============================] - 2s 524us/sample - loss: 1.2794e-05 - val_loss: 6.3769e-06
Epoch 422/1000
3888/3888 [==============================] - 2s 515us/sample - loss: 3.1247e-06 - val_loss: 1.8746e-06
Epoch 423/1000
3888/3888 [==============================] - 2s 526us/sample - loss: 7.1004e-06 - val_loss: 2.6985e-05
Epoch 424/1000
3888/3888 [==============================] - 2s 519us/sample - loss: 5.7805e-06 - val_loss: 2.3848e-06
Epoch 425/1000
3888/3888 [==============================] - 2s 522us/sample - loss: 3.8714e-06 - val_loss: 3.7967e-06
Epoch 426/1000
3888/3888 [==============================] - 2s 517us/sample - loss: 3.9854e-06 - val_loss: 2.9499e-06
Epoch 427/1000
3888/3888 [==============================] - 2s 525us/sample - loss: 8.4056e-06 - val_loss: 4.5954e-05
Epoch 428/1000
3888/3888 [==============================] - 2s 527us/sample - loss: 8.2245e-06 - val_loss: 2.6735e-06
Epoch 429/1000
3888/3888 [==============================] - 2s 531us/sample - loss: 3.6811e-06 - val_loss: 1.3119e-04
Epoch 430/1000
3888/3888 [==============================] - 2s 521us/sample - loss: 2.0221e-05 - val_loss: 1.9291e-06
Epoch 431/1000
3888/3888 [==============================] - 2s 522us/sample - loss: 2.2629e-06 - val_loss: 2.0363e-06
Epoch 432/1000
3888/3888 [==============================] - 2s 528us/sample - loss: 2.3224e-06 - val_loss: 4.2338e-06
Epoch 433/1000
3888/3888 [==============================] - 2s 526us/sample - loss: 3.7307e-06 - val_loss: 8.9199e-06
Epoch 434/1000
3888/3888 [==============================] - 2s 525us/sample - loss: 9.5896e-06 - val_loss: 4.3951e-06
Epoch 435/1000
3888/3888 [==============================] - 2s 516us/sample - loss: 2.3430e-06 - val_loss: 1.0217e-05
Epoch 436/1000
3888/3888 [==============================] - 2s 517us/sample - loss: 8.0118e-06 - val_loss: 4.1067e-06
Epoch 437/1000
3888/3888 [==============================] - 2s 524us/sample - loss: 7.6434e-06 - val_loss: 5.6278e-06
Epoch 438/1000
3888/3888 [==============================] - 2s 531us/sample - loss: 6.3661e-06 - val_loss: 4.5321e-06
Epoch 439/1000
3888/3888 [==============================] - 2s 519us/sample - loss: 3.9819e-06 - val_loss: 5.2804e-06
Epoch 440/1000
3888/3888 [==============================] - 2s 524us/sample - loss: 3.3630e-06 - val_loss: 6.4379e-06
Epoch 441/1000
3888/3888 [==============================] - 2s 507us/sample - loss: 6.2800e-06 - val_loss: 6.3206e-06
Epoch 442/1000
3888/3888 [==============================] - 2s 521us/sample - loss: 4.5450e-06 - val_loss: 3.4282e-05
Epoch 443/1000
3888/3888 [==============================] - 2s 516us/sample - loss: 1.2518e-05 - val_loss: 1.6223e-06
Epoch 444/1000
3888/3888 [==============================] - 2s 517us/sample - loss: 3.3875e-06 - val_loss: 3.1720e-06
Epoch 445/1000
3888/3888 [==============================] - 2s 511us/sample - loss: 1.6098e-05 - val_loss: 2.2443e-06
Epoch 446/1000
3888/3888 [==============================] - 2s 525us/sample - loss: 2.2748e-06 - val_loss: 2.0723e-05
Epoch 447/1000
3888/3888 [==============================] - 2s 529us/sample - loss: 6.6865e-06 - val_loss: 2.5630e-06
Epoch 448/1000
3888/3888 [==============================] - 2s 519us/sample - loss: 3.0153e-06 - val_loss: 2.3608e-06
Epoch 449/1000
3888/3888 [==============================] - 2s 522us/sample - loss: 5.3289e-06 - val_loss: 3.3566e-06
Epoch 450/1000
3888/3888 [==============================] - 2s 525us/sample - loss: 3.8928e-06 - val_loss: 5.7501e-06
Epoch 451/1000
3888/3888 [==============================] - 2s 519us/sample - loss: 6.3512e-06 - val_loss: 3.6718e-06
Epoch 452/1000
3888/3888 [==============================] - 2s 519us/sample - loss: 9.6717e-06 - val_loss: 2.2501e-06
Epoch 453/1000
3888/3888 [==============================] - 2s 525us/sample - loss: 4.0368e-06 - val_loss: 4.3232e-06
Epoch 454/1000
3888/3888 [==============================] - 2s 533us/sample - loss: 4.1641e-06 - val_loss: 1.0186e-04
Epoch 455/1000
3888/3888 [==============================] - 2s 523us/sample - loss: 1.2537e-05 - val_loss: 2.2803e-06
Epoch 456/1000
3888/3888 [==============================] - 2s 514us/sample - loss: 3.9934e-06 - val_loss: 2.7815e-06
Epoch 457/1000
3888/3888 [==============================] - 2s 531us/sample - loss: 1.0332e-05 - val_loss: 1.6721e-05
Epoch 458/1000
3888/3888 [==============================] - 2s 527us/sample - loss: 3.0069e-06 - val_loss: 1.7623e-06
Epoch 459/1000
3888/3888 [==============================] - 2s 521us/sample - loss: 6.7566e-06 - val_loss: 2.2205e-06
Epoch 460/1000
3888/3888 [==============================] - 2s 516us/sample - loss: 2.0756e-06 - val_loss: 1.5583e-06
Epoch 461/1000
3888/3888 [==============================] - 2s 511us/sample - loss: 4.6386e-06 - val_loss: 7.1363e-06
Epoch 462/1000
3888/3888 [==============================] - 2s 520us/sample - loss: 8.4558e-06 - val_loss: 2.7535e-06
Epoch 463/1000
3888/3888 [==============================] - 2s 512us/sample - loss: 1.5508e-05 - val_loss: 2.3925e-06
Epoch 464/1000
3888/3888 [==============================] - 2s 516us/sample - loss: 1.8030e-06 - val_loss: 2.4028e-06
Epoch 465/1000
3888/3888 [==============================] - 2s 510us/sample - loss: 2.4461e-06 - val_loss: 3.3196e-06
Epoch 466/1000
3888/3888 [==============================] - 2s 532us/sample - loss: 2.5914e-06 - val_loss: 5.0767e-06
Epoch 467/1000
3888/3888 [==============================] - 2s 518us/sample - loss: 9.7569e-06 - val_loss: 9.4748e-06
Epoch 468/1000
3888/3888 [==============================] - 2s 526us/sample - loss: 4.2160e-06 - val_loss: 1.6647e-06
Epoch 469/1000
3888/3888 [==============================] - 2s 512us/sample - loss: 4.7615e-06 - val_loss: 1.6990e-06
Epoch 470/1000
3888/3888 [==============================] - 2s 517us/sample - loss: 7.4687e-06 - val_loss: 1.5413e-05
Epoch 471/1000
3888/3888 [==============================] - 2s 510us/sample - loss: 8.6830e-06 - val_loss: 1.5755e-06
Epoch 472/1000
3888/3888 [==============================] - 2s 515us/sample - loss: 2.1471e-06 - val_loss: 1.6020e-06
Epoch 473/1000
3888/3888 [==============================] - 2s 513us/sample - loss: 1.2604e-05 - val_loss: 8.9875e-06
Epoch 474/1000
3888/3888 [==============================] - 2s 518us/sample - loss: 3.3254e-06 - val_loss: 4.6769e-06
Epoch 475/1000
3888/3888 [==============================] - 2s 521us/sample - loss: 3.5474e-06 - val_loss: 2.2678e-06
Epoch 476/1000
3888/3888 [==============================] - 2s 526us/sample - loss: 6.1097e-06 - val_loss: 2.5484e-05
Epoch 477/1000
3888/3888 [==============================] - 2s 527us/sample - loss: 5.3429e-06 - val_loss: 2.2617e-06
Epoch 478/1000
3888/3888 [==============================] - 2s 534us/sample - loss: 4.6878e-06 - val_loss: 6.2972e-06
Epoch 479/1000
3888/3888 [==============================] - 2s 516us/sample - loss: 7.5191e-06 - val_loss: 1.8592e-06
Epoch 480/1000
3888/3888 [==============================] - 2s 518us/sample - loss: 2.8433e-06 - val_loss: 3.1215e-06
Epoch 481/1000
3888/3888 [==============================] - 2s 533us/sample - loss: 8.3542e-06 - val_loss: 8.2867e-06
Epoch 482/1000
3888/3888 [==============================] - 2s 507us/sample - loss: 5.5501e-06 - val_loss: 1.5051e-05
Epoch 483/1000
3888/3888 [==============================] - 2s 521us/sample - loss: 4.3921e-06 - val_loss: 2.7111e-05
Epoch 484/1000
3888/3888 [==============================] - 2s 521us/sample - loss: 6.0440e-06 - val_loss: 2.0719e-06
Epoch 485/1000
3888/3888 [==============================] - 2s 528us/sample - loss: 3.7278e-06 - val_loss: 1.3995e-06
Epoch 486/1000
3888/3888 [==============================] - 2s 512us/sample - loss: 8.2209e-06 - val_loss: 4.0150e-06
Epoch 487/1000
3888/3888 [==============================] - 2s 528us/sample - loss: 3.9177e-06 - val_loss: 4.7908e-06
Epoch 488/1000
3888/3888 [==============================] - 2s 522us/sample - loss: 6.6267e-06 - val_loss: 1.0626e-04
Epoch 489/1000
3888/3888 [==============================] - 2s 525us/sample - loss: 1.0011e-05 - val_loss: 1.6490e-06
Epoch 490/1000
3888/3888 [==============================] - 2s 509us/sample - loss: 2.3579e-06 - val_loss: 2.7779e-06
Epoch 491/1000
3888/3888 [==============================] - 2s 511us/sample - loss: 1.3324e-05 - val_loss: 1.9043e-06
Epoch 492/1000
3888/3888 [==============================] - 2s 528us/sample - loss: 2.3793e-06 - val_loss: 2.4515e-05
Epoch 493/1000
3888/3888 [==============================] - 2s 525us/sample - loss: 4.4677e-06 - val_loss: 1.5414e-06
Epoch 494/1000
3888/3888 [==============================] - 2s 507us/sample - loss: 1.3537e-05 - val_loss: 3.1978e-06
Epoch 495/1000
3888/3888 [==============================] - 2s 507us/sample - loss: 4.0056e-06 - val_loss: 2.3614e-06
Epoch 496/1000
3888/3888 [==============================] - 2s 514us/sample - loss: 2.0211e-06 - val_loss: 1.4953e-06
Epoch 497/1000
3888/3888 [==============================] - 2s 528us/sample - loss: 5.8399e-06 - val_loss: 2.4535e-06
Epoch 498/1000
3888/3888 [==============================] - 2s 532us/sample - loss: 5.3319e-06 - val_loss: 8.2319e-06
Epoch 499/1000
3888/3888 [==============================] - 2s 517us/sample - loss: 1.3444e-05 - val_loss: 1.4691e-06
Epoch 500/1000
3888/3888 [==============================] - 2s 532us/sample - loss: 1.5649e-06 - val_loss: 3.8898e-06
Epoch 501/1000
3888/3888 [==============================] - 2s 524us/sample - loss: 1.7725e-06 - val_loss: 1.3053e-06
Epoch 502/1000
3888/3888 [==============================] - 2s 517us/sample - loss: 9.5350e-06 - val_loss: 2.9043e-06
Epoch 503/1000
3888/3888 [==============================] - 2s 520us/sample - loss: 3.0681e-06 - val_loss: 3.8133e-06
Epoch 504/1000
3888/3888 [==============================] - 2s 514us/sample - loss: 4.8627e-06 - val_loss: 1.2745e-05
Epoch 505/1000
3888/3888 [==============================] - 2s 515us/sample - loss: 8.1209e-06 - val_loss: 2.9752e-06
Epoch 506/1000
3888/3888 [==============================] - 2s 520us/sample - loss: 2.9745e-06 - val_loss: 4.6810e-06
Epoch 507/1000
3888/3888 [==============================] - 2s 516us/sample - loss: 1.1783e-05 - val_loss: 2.1081e-06
Epoch 508/1000
3888/3888 [==============================] - 2s 526us/sample - loss: 1.9571e-06 - val_loss: 1.6244e-06
Epoch 509/1000
3888/3888 [==============================] - 2s 518us/sample - loss: 5.2568e-06 - val_loss: 1.1290e-04
Epoch 510/1000
3888/3888 [==============================] - 2s 524us/sample - loss: 7.9201e-06 - val_loss: 3.9374e-06
Epoch 511/1000
3888/3888 [==============================] - 2s 515us/sample - loss: 2.5577e-06 - val_loss: 4.4031e-06
Epoch 512/1000
3888/3888 [==============================] - 2s 527us/sample - loss: 8.5399e-06 - val_loss: 4.7891e-06
Epoch 513/1000
3888/3888 [==============================] - 2s 521us/sample - loss: 2.9897e-06 - val_loss: 5.3012e-06
Epoch 514/1000
3888/3888 [==============================] - 2s 517us/sample - loss: 1.4015e-05 - val_loss: 4.5322e-05
Epoch 515/1000
3888/3888 [==============================] - 2s 524us/sample - loss: 2.8631e-06 - val_loss: 2.2241e-06
Epoch 516/1000
3888/3888 [==============================] - 2s 520us/sample - loss: 2.3498e-06 - val_loss: 2.0121e-06
Epoch 517/1000
3888/3888 [==============================] - 2s 523us/sample - loss: 6.3863e-06 - val_loss: 5.5098e-06
Epoch 518/1000
3888/3888 [==============================] - 2s 513us/sample - loss: 4.0633e-06 - val_loss: 3.0095e-06
Epoch 519/1000
3888/3888 [==============================] - 2s 519us/sample - loss: 1.9980e-05 - val_loss: 2.1042e-06
Epoch 520/1000
3888/3888 [==============================] - 2s 509us/sample - loss: 1.4994e-06 - val_loss: 1.3542e-06
Epoch 521/1000
3888/3888 [==============================] - 2s 525us/sample - loss: 1.5451e-06 - val_loss: 1.7695e-06
Epoch 522/1000
3888/3888 [==============================] - 2s 514us/sample - loss: 8.3427e-06 - val_loss: 2.0564e-06
Epoch 523/1000
3888/3888 [==============================] - 2s 519us/sample - loss: 2.7872e-06 - val_loss: 1.4690e-06
Epoch 524/1000
3888/3888 [==============================] - 2s 514us/sample - loss: 2.2669e-06 - val_loss: 3.4363e-06
Epoch 525/1000
3888/3888 [==============================] - 2s 530us/sample - loss: 3.3306e-06 - val_loss: 5.6139e-06
Epoch 526/1000
3888/3888 [==============================] - 2s 525us/sample - loss: 7.5951e-06 - val_loss: 5.0140e-06
Epoch 527/1000
3888/3888 [==============================] - 2s 510us/sample - loss: 2.6646e-06 - val_loss: 5.8321e-06
Epoch 528/1000
3888/3888 [==============================] - 2s 517us/sample - loss: 7.6137e-06 - val_loss: 2.4344e-06
Epoch 529/1000
3888/3888 [==============================] - 2s 521us/sample - loss: 2.2535e-06 - val_loss: 2.2041e-06
Epoch 530/1000
3888/3888 [==============================] - 2s 521us/sample - loss: 1.0164e-05 - val_loss: 9.7516e-05
Epoch 531/1000
3888/3888 [==============================] - 2s 519us/sample - loss: 5.0279e-06 - val_loss: 4.8506e-06
Epoch 532/1000
3888/3888 [==============================] - 2s 514us/sample - loss: 4.5835e-06 - val_loss: 1.8972e-06
Epoch 533/1000
3888/3888 [==============================] - 2s 511us/sample - loss: 7.7115e-06 - val_loss: 1.3270e-05
Epoch 534/1000
3888/3888 [==============================] - 2s 520us/sample - loss: 1.1392e-05 - val_loss: 1.5546e-05
Epoch 535/1000
3888/3888 [==============================] - 2s 526us/sample - loss: 2.4053e-06 - val_loss: 1.5311e-06
Epoch 536/1000
3888/3888 [==============================] - 2s 520us/sample - loss: 2.5473e-06 - val_loss: 1.1458e-05
Epoch 537/1000
3888/3888 [==============================] - 2s 524us/sample - loss: 3.8128e-06 - val_loss: 2.0542e-06
Epoch 538/1000
3888/3888 [==============================] - 2s 520us/sample - loss: 6.2003e-06 - val_loss: 7.1601e-06
Epoch 539/1000
3888/3888 [==============================] - 2s 519us/sample - loss: 4.9087e-06 - val_loss: 1.1894e-05
Epoch 540/1000
3888/3888 [==============================] - 2s 525us/sample - loss: 7.3835e-06 - val_loss: 1.5244e-06
Epoch 541/1000
3888/3888 [==============================] - 2s 524us/sample - loss: 2.2890e-06 - val_loss: 5.2968e-05
Epoch 542/1000
3888/3888 [==============================] - 2s 519us/sample - loss: 7.3445e-06 - val_loss: 3.4704e-06
Epoch 543/1000
3888/3888 [==============================] - 2s 521us/sample - loss: 5.0404e-06 - val_loss: 7.4623e-06
Epoch 544/1000
3888/3888 [==============================] - 2s 527us/sample - loss: 1.3650e-05 - val_loss: 8.8416e-06
Epoch 545/1000
3888/3888 [==============================] - 2s 533us/sample - loss: 1.8725e-06 - val_loss: 3.1696e-06
Epoch 546/1000
3888/3888 [==============================] - 2s 523us/sample - loss: 3.7403e-06 - val_loss: 2.1714e-05
Epoch 547/1000
3888/3888 [==============================] - 2s 516us/sample - loss: 2.7124e-06 - val_loss: 3.8430e-06
Epoch 548/1000
3888/3888 [==============================] - 2s 526us/sample - loss: 5.3578e-06 - val_loss: 1.6417e-06
Epoch 549/1000
3888/3888 [==============================] - 2s 518us/sample - loss: 3.8552e-06 - val_loss: 5.7091e-06
Epoch 550/1000
3888/3888 [==============================] - 2s 522us/sample - loss: 5.8519e-06 - val_loss: 3.7367e-06
Epoch 551/1000
3888/3888 [==============================] - 2s 520us/sample - loss: 3.6996e-06 - val_loss: 1.0334e-05
Epoch 552/1000
3888/3888 [==============================] - 2s 512us/sample - loss: 8.2127e-06 - val_loss: 5.0946e-06
Epoch 553/1000
3888/3888 [==============================] - 2s 526us/sample - loss: 8.8551e-06 - val_loss: 1.0220e-05
Epoch 554/1000
3888/3888 [==============================] - 2s 522us/sample - loss: 4.1572e-06 - val_loss: 1.3035e-05
Epoch 555/1000
3888/3888 [==============================] - 2s 521us/sample - loss: 4.1485e-06 - val_loss: 9.0559e-06
Epoch 556/1000
3888/3888 [==============================] - 2s 514us/sample - loss: 4.9445e-06 - val_loss: 1.7113e-06
Epoch 557/1000
3888/3888 [==============================] - 2s 515us/sample - loss: 3.7571e-06 - val_loss: 2.9121e-06
Epoch 558/1000
3888/3888 [==============================] - 2s 518us/sample - loss: 4.2609e-06 - val_loss: 3.2241e-06
Epoch 559/1000
3888/3888 [==============================] - 2s 515us/sample - loss: 1.1203e-05 - val_loss: 5.7351e-06
Epoch 560/1000
3888/3888 [==============================] - 2s 517us/sample - loss: 2.1763e-06 - val_loss: 2.3035e-06
Epoch 561/1000
3888/3888 [==============================] - 2s 524us/sample - loss: 3.1797e-06 - val_loss: 4.4746e-06
Epoch 562/1000
3888/3888 [==============================] - 2s 525us/sample - loss: 3.3246e-06 - val_loss: 3.8687e-06
Epoch 563/1000
3888/3888 [==============================] - 2s 527us/sample - loss: 7.1640e-06 - val_loss: 1.8002e-06
Epoch 564/1000
3888/3888 [==============================] - 2s 516us/sample - loss: 3.5687e-06 - val_loss: 1.7234e-06
Epoch 565/1000
3888/3888 [==============================] - 2s 519us/sample - loss: 6.7577e-06 - val_loss: 2.9982e-05
Epoch 566/1000
3888/3888 [==============================] - 2s 530us/sample - loss: 4.3330e-06 - val_loss: 6.5001e-06
Epoch 567/1000
3888/3888 [==============================] - 2s 520us/sample - loss: 4.7838e-06 - val_loss: 1.4490e-06
Epoch 568/1000
3888/3888 [==============================] - 2s 519us/sample - loss: 7.5817e-06 - val_loss: 9.6791e-06
Epoch 569/1000
3888/3888 [==============================] - 2s 520us/sample - loss: 8.5611e-06 - val_loss: 6.9920e-06
Epoch 570/1000
3888/3888 [==============================] - 2s 514us/sample - loss: 3.2533e-06 - val_loss: 3.1448e-06
Epoch 571/1000
3888/3888 [==============================] - 2s 520us/sample - loss: 1.9123e-06 - val_loss: 2.6444e-06
Epoch 572/1000
3888/3888 [==============================] - 2s 525us/sample - loss: 1.1447e-05 - val_loss: 1.8297e-06
Epoch 573/1000
3888/3888 [==============================] - 2s 520us/sample - loss: 3.1469e-06 - val_loss: 1.6002e-06
Epoch 574/1000
3888/3888 [==============================] - 2s 517us/sample - loss: 1.0359e-05 - val_loss: 9.1355e-06
Epoch 575/1000
3888/3888 [==============================] - 2s 527us/sample - loss: 1.7074e-06 - val_loss: 1.6155e-06
Epoch 576/1000
3888/3888 [==============================] - 2s 529us/sample - loss: 1.7158e-06 - val_loss: 2.4014e-06
Epoch 577/1000
3888/3888 [==============================] - 2s 512us/sample - loss: 3.2391e-06 - val_loss: 4.4465e-06
Epoch 578/1000
3888/3888 [==============================] - 2s 529us/sample - loss: 1.0660e-05 - val_loss: 2.1366e-06
Epoch 579/1000
3888/3888 [==============================] - 2s 519us/sample - loss: 3.9415e-06 - val_loss: 6.3034e-06
Epoch 580/1000
3888/3888 [==============================] - 2s 520us/sample - loss: 3.2084e-06 - val_loss: 5.3064e-06
Epoch 581/1000
3888/3888 [==============================] - 2s 530us/sample - loss: 4.1859e-06 - val_loss: 4.1636e-06
Epoch 582/1000
3888/3888 [==============================] - 2s 524us/sample - loss: 5.3319e-06 - val_loss: 2.0582e-06
Epoch 583/1000
3888/3888 [==============================] - 2s 531us/sample - loss: 6.4532e-06 - val_loss: 4.0206e-06
Epoch 584/1000
3888/3888 [==============================] - 2s 527us/sample - loss: 6.1445e-06 - val_loss: 2.4975e-06
Epoch 585/1000
3888/3888 [==============================] - 2s 513us/sample - loss: 2.9030e-06 - val_loss: 2.0058e-06
Epoch 586/1000
3888/3888 [==============================] - 2s 509us/sample - loss: 7.5439e-06 - val_loss: 4.5810e-06
Epoch 587/1000
3888/3888 [==============================] - 2s 526us/sample - loss: 3.2171e-06 - val_loss: 3.8658e-06
Epoch 588/1000
3888/3888 [==============================] - 2s 525us/sample - loss: 5.4219e-06 - val_loss: 2.3746e-06
Epoch 589/1000
3888/3888 [==============================] - 2s 536us/sample - loss: 4.5144e-06 - val_loss: 2.5607e-06
Epoch 590/1000
3888/3888 [==============================] - 2s 518us/sample - loss: 4.0862e-06 - val_loss: 1.9989e-06
Epoch 591/1000
3888/3888 [==============================] - 2s 525us/sample - loss: 1.1080e-05 - val_loss: 2.2968e-06
Epoch 592/1000
3888/3888 [==============================] - 2s 530us/sample - loss: 2.8358e-06 - val_loss: 7.2369e-06
Epoch 593/1000
3888/3888 [==============================] - 2s 516us/sample - loss: 5.8478e-06 - val_loss: 7.0032e-06
Epoch 594/1000
3888/3888 [==============================] - 2s 518us/sample - loss: 4.4308e-06 - val_loss: 4.2108e-06
Epoch 595/1000
3888/3888 [==============================] - 2s 526us/sample - loss: 3.5390e-06 - val_loss: 2.0974e-06
Epoch 596/1000
3888/3888 [==============================] - 2s 520us/sample - loss: 9.5044e-06 - val_loss: 4.3031e-05
Epoch 597/1000
3888/3888 [==============================] - 2s 536us/sample - loss: 7.6600e-06 - val_loss: 2.7332e-06
Epoch 598/1000
3888/3888 [==============================] - 2s 519us/sample - loss: 1.4903e-06 - val_loss: 1.3590e-06
Epoch 599/1000
3888/3888 [==============================] - 2s 536us/sample - loss: 2.4768e-06 - val_loss: 7.2678e-05
Epoch 600/1000
3888/3888 [==============================] - 2s 528us/sample - loss: 7.0254e-06 - val_loss: 4.8043e-06
Epoch 601/1000
3840/3888 [============================>.] - ETA: 0s - loss: 2.6166e-06Restoring model weights from the end of the best epoch.
3888/3888 [==============================] - 2s 507us/sample - loss: 2.5999e-06 - val_loss: 1.8691e-06
Epoch 00601: early stopping
In [63]:
print(history.history.keys())
print('best value: ', conv_ae.evaluate(X_train, X_train, verbose=0))


pd.DataFrame(history.history).plot(figsize=(8, 5), logy=True)
plt.grid()
dict_keys(['loss', 'val_loss'])
best value:  1.3052720110870932e-06
In [64]:
X_reconstructions = conv_ae.predict(X_train)
X_reconstructions = stdscaler.inverse_transform(np.moveaxis(X_reconstructions,3,1).reshape(len(times),len(group)*nl*nc))
calculateerror(X_train_1D.reshape(len(times),len(groups),nl,nc), 
               X_reconstructions.reshape(len(times),len(groups),nl,nc), 
               groups,
               print_step=0)
max_abs_error:  6.6978759765625
mean_abs_error:  0.015144726812325737
/home/viluiz/anaconda3/envs/py3ml/lib/python3.7/site-packages/ipykernel_launcher.py:3: RuntimeWarning: divide by zero encountered in true_divide
  This is separate from the ipykernel package so we can avoid doing imports until
In [65]:
fig, ax = plt.subplots(2,4, figsize=[20,10])
for i, group in enumerate(groups):
    im = ax.flatten()[i].imshow(X_reconstructions.reshape(len(times),len(groups),nl,nc)[100,i,:,:])
    fig.colorbar(im, ax=ax.flatten()[i])
    ax.flatten()[i].set_title(group)
In [66]:
fig, ax = plt.subplots(2,4, figsize=[20,10])
for i, group in enumerate(groups):
    ax.flatten()[i].plot(times, X_train_1D[:,i*nl*nc+4])
    ax.flatten()[i].plot(times, X_reconstructions[:,i*nl*nc+4],'--')
    ax.flatten()[i].set_title(group)
In [67]:
tf.random.set_seed(42)
np.random.seed(42)

# Need to have validation loss
early_stopping = keras.callbacks.EarlyStopping(monitor='val_loss',
                                               min_delta=0.0,
                                               patience=100,
                                               verbose=2,
                                               restore_best_weights=True)

conv_encoder = keras.models.Sequential([
    keras.layers.Reshape([10, 10, 8, 1], input_shape=[10, 10, 8]),
    #keras.layers.InputLayer(input_shape=(10, 10, 8)),
    keras.layers.Conv3D(64, kernel_size=3, padding="SAME", activation="elu"),
    keras.layers.MaxPool3D(pool_size=2),
    keras.layers.Conv3D(64, kernel_size=3, padding="SAME", activation="elu"),
    keras.layers.MaxPool3D(pool_size=2),
    keras.layers.Conv3D(64, kernel_size=3, padding="SAME", activation="elu"),
    keras.layers.MaxPool3D(pool_size=2),
    keras.layers.Flatten(),
    #keras.layers.Dense(64, activation="selu", kernel_initializer="lecun_normal"),
    keras.layers.Dense(15)
])
conv_decoder = keras.models.Sequential([
    keras.layers.InputLayer(input_shape=(15)),
    #keras.layers.Dense(64, activation="selu", kernel_initializer="lecun_normal"),
    keras.layers.Dense(64*1*1*1, activation="elu"),
    keras.layers.Reshape(target_shape=(1, 1, 1, 64)),
    keras.layers.Conv3DTranspose(64, kernel_size=3, strides=2, padding="SAME", activation="elu"),
    keras.layers.Conv3DTranspose(64, kernel_size=3, strides=3, padding="SAME", output_padding=[1,1,0], activation="elu"),
    keras.layers.Conv3DTranspose(1, kernel_size=3, strides=2, padding="SAME"),
    keras.layers.Reshape([10, 10, 8])
])

conv_ae = keras.models.Sequential([conv_encoder, conv_decoder])
conv_ae.compile(loss="mse", 
                optimizer=keras.optimizers.Nadam(lr=0.0003, beta_1=0.9, beta_2=0.999))

conv_encoder.summary()
conv_decoder.summary()
Model: "sequential_15"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
reshape_2 (Reshape)          (None, 10, 10, 8, 1)      0         
_________________________________________________________________
conv3d (Conv3D)              (None, 10, 10, 8, 64)     1792      
_________________________________________________________________
max_pooling3d (MaxPooling3D) (None, 5, 5, 4, 64)       0         
_________________________________________________________________
conv3d_1 (Conv3D)            (None, 5, 5, 4, 64)       110656    
_________________________________________________________________
max_pooling3d_1 (MaxPooling3 (None, 2, 2, 2, 64)       0         
_________________________________________________________________
conv3d_2 (Conv3D)            (None, 2, 2, 2, 64)       110656    
_________________________________________________________________
max_pooling3d_2 (MaxPooling3 (None, 1, 1, 1, 64)       0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 64)                0         
_________________________________________________________________
dense_24 (Dense)             (None, 15)                975       
=================================================================
Total params: 224,079
Trainable params: 224,079
Non-trainable params: 0
_________________________________________________________________
Model: "sequential_16"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_25 (Dense)             (None, 64)                1024      
_________________________________________________________________
reshape_3 (Reshape)          (None, 1, 1, 1, 64)       0         
_________________________________________________________________
conv3d_transpose (Conv3DTran (None, 2, 2, 2, 64)       110656    
_________________________________________________________________
conv3d_transpose_1 (Conv3DTr (None, 5, 5, 4, 64)       110656    
_________________________________________________________________
conv3d_transpose_2 (Conv3DTr (None, 10, 10, 8, 1)      1729      
_________________________________________________________________
reshape_4 (Reshape)          (None, 10, 10, 8)         0         
=================================================================
Total params: 224,065
Trainable params: 224,065
Non-trainable params: 0
_________________________________________________________________
In [68]:
history = conv_ae.fit(X_train, X_train, 
                      epochs=1000, 
                      validation_data=(X_train, X_train),
                      callbacks=[early_stopping])
Train on 3888 samples, validate on 3888 samples
Epoch 1/1000
3888/3888 [==============================] - 27s 7ms/sample - loss: 0.0645 - val_loss: 0.0179
Epoch 2/1000
3888/3888 [==============================] - 25s 7ms/sample - loss: 0.0133 - val_loss: 0.0085
Epoch 3/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 0.0037 - val_loss: 0.0013
Epoch 4/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 0.0013 - val_loss: 6.0102e-04
Epoch 5/1000
3888/3888 [==============================] - 25s 7ms/sample - loss: 7.0133e-04 - val_loss: 4.6277e-04
Epoch 6/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 6.3439e-04 - val_loss: 2.6882e-04
Epoch 7/1000
3888/3888 [==============================] - 25s 7ms/sample - loss: 6.1017e-04 - val_loss: 3.0444e-04
Epoch 8/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.3062e-04 - val_loss: 2.3250e-04
Epoch 9/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.2092e-04 - val_loss: 1.4888e-04
Epoch 10/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.7692e-04 - val_loss: 0.0011
Epoch 11/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.2421e-04 - val_loss: 1.9130e-04
Epoch 12/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.0109e-04 - val_loss: 3.9702e-04
Epoch 13/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.8472e-04 - val_loss: 8.7998e-05
Epoch 14/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.6230e-04 - val_loss: 3.5600e-04
Epoch 15/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.4391e-04 - val_loss: 1.8714e-04
Epoch 16/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.7324e-04 - val_loss: 1.1555e-04
Epoch 17/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.9702e-04 - val_loss: 6.2670e-05
Epoch 18/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 7.3896e-05 - val_loss: 9.0681e-05
Epoch 19/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.9174e-04 - val_loss: 6.6589e-05
Epoch 20/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 7.7725e-05 - val_loss: 5.5764e-05
Epoch 21/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.5372e-04 - val_loss: 4.7542e-05
Epoch 22/1000
3888/3888 [==============================] - 25s 7ms/sample - loss: 9.9465e-05 - val_loss: 7.8597e-05
Epoch 23/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.0755e-04 - val_loss: 4.5444e-05
Epoch 24/1000
3888/3888 [==============================] - 25s 7ms/sample - loss: 1.3892e-04 - val_loss: 4.4189e-05
Epoch 25/1000
3888/3888 [==============================] - 25s 7ms/sample - loss: 1.1894e-04 - val_loss: 6.5300e-05
Epoch 26/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.2847e-04 - val_loss: 4.6664e-04
Epoch 27/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 8.2728e-05 - val_loss: 7.5831e-05
Epoch 28/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.8525e-04 - val_loss: 9.8349e-05
Epoch 29/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.0758e-04 - val_loss: 4.4537e-05
Epoch 30/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.7128e-04 - val_loss: 1.1983e-04
Epoch 31/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.0244e-04 - val_loss: 4.9485e-05
Epoch 32/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 7.5086e-05 - val_loss: 4.3630e-05
Epoch 33/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.8355e-04 - val_loss: 3.9957e-05
Epoch 34/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.6548e-05 - val_loss: 3.1633e-05
Epoch 35/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.0646e-05 - val_loss: 7.3582e-05
Epoch 36/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.4759e-05 - val_loss: 3.8306e-04
Epoch 37/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 8.6051e-05 - val_loss: 2.8212e-05
Epoch 38/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.6551e-04 - val_loss: 2.6479e-05
Epoch 39/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.4648e-05 - val_loss: 2.4684e-05
Epoch 40/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 7.1989e-05 - val_loss: 3.7292e-05
Epoch 41/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.0074e-04 - val_loss: 6.6136e-05
Epoch 42/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 8.2945e-05 - val_loss: 0.0019
Epoch 43/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.4815e-04 - val_loss: 3.5411e-04
Epoch 44/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 9.7046e-05 - val_loss: 1.3403e-04
Epoch 45/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 6.8387e-05 - val_loss: 1.9411e-05
Epoch 46/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.1307e-05 - val_loss: 2.5702e-05
Epoch 47/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.6230e-04 - val_loss: 2.9720e-05
Epoch 48/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 7.0510e-05 - val_loss: 1.6476e-05
Epoch 49/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.1958e-04 - val_loss: 2.2765e-05
Epoch 50/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.9401e-05 - val_loss: 1.3560e-04
Epoch 51/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.7963e-05 - val_loss: 8.4380e-05
Epoch 52/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.3049e-04 - val_loss: 1.8876e-05
Epoch 53/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.8607e-05 - val_loss: 1.7366e-05
Epoch 54/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 9.7766e-05 - val_loss: 7.3659e-05
Epoch 55/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.9747e-05 - val_loss: 1.3606e-05
Epoch 56/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.6305e-05 - val_loss: 2.3468e-05
Epoch 57/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.5085e-05 - val_loss: 2.7569e-05
Epoch 58/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 6.7372e-05 - val_loss: 3.8003e-04
Epoch 59/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 9.9448e-05 - val_loss: 2.3421e-05
Epoch 60/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.8261e-05 - val_loss: 1.8667e-05
Epoch 61/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 9.9535e-05 - val_loss: 5.7787e-05
Epoch 62/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 8.4707e-05 - val_loss: 1.2709e-04
Epoch 63/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.0013e-05 - val_loss: 1.5185e-05
Epoch 64/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.9634e-05 - val_loss: 2.4384e-05
Epoch 65/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.6261e-05 - val_loss: 3.4582e-04
Epoch 66/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 7.0195e-05 - val_loss: 2.3718e-05
Epoch 67/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.7817e-04 - val_loss: 1.6552e-04
Epoch 68/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.7320e-05 - val_loss: 1.1497e-05
Epoch 69/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.7949e-04 - val_loss: 2.0849e-05
Epoch 70/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.6398e-05 - val_loss: 1.0609e-05
Epoch 71/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.0537e-05 - val_loss: 1.0104e-05
Epoch 72/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.5248e-05 - val_loss: 8.1880e-06
Epoch 73/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.8968e-05 - val_loss: 2.1065e-05
Epoch 74/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.5661e-05 - val_loss: 1.1680e-05
Epoch 75/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.7603e-05 - val_loss: 4.5737e-05
Epoch 76/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.7768e-05 - val_loss: 7.1735e-06
Epoch 77/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.7030e-05 - val_loss: 1.6333e-04
Epoch 78/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 6.2020e-05 - val_loss: 1.7602e-05
Epoch 79/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.5399e-05 - val_loss: 6.1720e-04
Epoch 80/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.7780e-04 - val_loss: 1.1774e-05
Epoch 81/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.0181e-05 - val_loss: 1.3716e-05
Epoch 82/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.0030e-05 - val_loss: 1.4008e-05
Epoch 83/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.9977e-05 - val_loss: 7.6291e-06
Epoch 84/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 7.5692e-05 - val_loss: 9.0191e-06
Epoch 85/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.3797e-05 - val_loss: 1.3189e-05
Epoch 86/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.1041e-05 - val_loss: 7.2317e-06
Epoch 87/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.5371e-05 - val_loss: 1.1684e-05
Epoch 88/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 7.9545e-05 - val_loss: 8.6677e-04
Epoch 89/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.8523e-05 - val_loss: 1.1052e-05
Epoch 90/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.3515e-05 - val_loss: 8.0050e-06
Epoch 91/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.7114e-05 - val_loss: 1.7020e-05
Epoch 92/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.5624e-05 - val_loss: 2.5560e-05
Epoch 93/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.3934e-05 - val_loss: 1.1292e-05
Epoch 94/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 9.4439e-06 - val_loss: 3.1204e-05
Epoch 95/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 7.4749e-05 - val_loss: 9.1337e-06
Epoch 96/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.4868e-05 - val_loss: 3.6920e-05
Epoch 97/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.3189e-05 - val_loss: 3.5045e-05
Epoch 98/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.3189e-05 - val_loss: 1.6266e-05
Epoch 99/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.0070e-04 - val_loss: 5.8634e-04
Epoch 100/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 7.5690e-05 - val_loss: 6.6018e-06
Epoch 101/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 9.0481e-06 - val_loss: 1.5554e-05
Epoch 102/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.3833e-05 - val_loss: 6.8025e-06
Epoch 103/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.1319e-05 - val_loss: 1.0299e-05
Epoch 104/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.4185e-05 - val_loss: 2.4188e-05
Epoch 105/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.4697e-05 - val_loss: 7.7796e-06
Epoch 106/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.9058e-05 - val_loss: 7.4796e-06
Epoch 107/1000
3888/3888 [==============================] - 25s 7ms/sample - loss: 7.8816e-06 - val_loss: 6.0071e-06
Epoch 108/1000
3888/3888 [==============================] - 25s 7ms/sample - loss: 4.0847e-05 - val_loss: 6.6178e-06
Epoch 109/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 6.3560e-05 - val_loss: 1.8455e-05
Epoch 110/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.3484e-05 - val_loss: 5.9129e-06
Epoch 111/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.5071e-05 - val_loss: 7.2685e-06
Epoch 112/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 6.9222e-06 - val_loss: 7.1301e-06
Epoch 113/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.0648e-05 - val_loss: 7.6133e-06
Epoch 114/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.7882e-05 - val_loss: 4.7872e-05
Epoch 115/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.0517e-05 - val_loss: 5.6864e-06
Epoch 116/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.6399e-05 - val_loss: 6.0527e-05
Epoch 117/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.6349e-05 - val_loss: 8.2738e-06
Epoch 118/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.0886e-05 - val_loss: 1.0745e-05
Epoch 119/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.6089e-05 - val_loss: 6.0677e-05
Epoch 120/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.8486e-05 - val_loss: 1.2740e-05
Epoch 121/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.0335e-05 - val_loss: 2.3188e-04
Epoch 122/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.2030e-05 - val_loss: 5.5540e-06
Epoch 123/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.8171e-05 - val_loss: 2.8626e-05
Epoch 124/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.9201e-05 - val_loss: 8.6804e-06
Epoch 125/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.6662e-05 - val_loss: 1.5689e-05
Epoch 126/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.9150e-05 - val_loss: 6.2886e-06
Epoch 127/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.6865e-05 - val_loss: 2.0184e-04
Epoch 128/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.3615e-05 - val_loss: 1.4977e-05
Epoch 129/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.1761e-05 - val_loss: 5.7443e-06
Epoch 130/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 6.9165e-06 - val_loss: 3.8631e-05
Epoch 131/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.4477e-05 - val_loss: 1.1585e-05
Epoch 132/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.7978e-05 - val_loss: 1.1998e-05
Epoch 133/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.0304e-05 - val_loss: 1.5677e-05
Epoch 134/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 6.0340e-05 - val_loss: 1.2288e-05
Epoch 135/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 6.1057e-06 - val_loss: 7.3793e-06
Epoch 136/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.3524e-05 - val_loss: 5.2595e-06
Epoch 137/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.5441e-05 - val_loss: 3.7844e-05
Epoch 138/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.3787e-05 - val_loss: 6.7717e-06
Epoch 139/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.3705e-05 - val_loss: 1.5190e-05
Epoch 140/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.4421e-05 - val_loss: 2.1593e-05
Epoch 141/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.9834e-05 - val_loss: 9.6887e-05
Epoch 142/1000
3888/3888 [==============================] - 25s 7ms/sample - loss: 1.1577e-05 - val_loss: 4.2448e-06
Epoch 143/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.0103e-05 - val_loss: 1.8027e-05
Epoch 144/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 7.2469e-06 - val_loss: 9.7967e-06
Epoch 145/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 9.0995e-05 - val_loss: 4.9933e-06
Epoch 146/1000
3888/3888 [==============================] - 25s 7ms/sample - loss: 4.4109e-06 - val_loss: 3.6676e-06
Epoch 147/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.9316e-06 - val_loss: 5.0144e-06
Epoch 148/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.5957e-05 - val_loss: 1.7697e-05
Epoch 149/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.1266e-05 - val_loss: 1.3042e-05
Epoch 150/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.0068e-05 - val_loss: 1.2905e-04
Epoch 151/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 8.9922e-06 - val_loss: 4.5736e-06
Epoch 152/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 6.9870e-06 - val_loss: 1.2870e-05
Epoch 153/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.7642e-05 - val_loss: 9.4309e-06
Epoch 154/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 6.8421e-06 - val_loss: 3.7391e-05
Epoch 155/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.8295e-05 - val_loss: 6.5325e-06
Epoch 156/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.5449e-06 - val_loss: 8.1202e-06
Epoch 157/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.9095e-05 - val_loss: 3.6105e-06
Epoch 158/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.4223e-06 - val_loss: 4.1640e-06
Epoch 159/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.2373e-06 - val_loss: 1.1816e-05
Epoch 160/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 9.2474e-06 - val_loss: 2.4496e-05
Epoch 161/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.7444e-05 - val_loss: 1.0854e-05
Epoch 162/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.0934e-05 - val_loss: 2.4965e-05
Epoch 163/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.7011e-05 - val_loss: 4.4553e-06
Epoch 164/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.3902e-05 - val_loss: 5.8486e-06
Epoch 165/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.0193e-05 - val_loss: 2.5035e-05
Epoch 166/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 6.3038e-05 - val_loss: 4.5889e-06
Epoch 167/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.3911e-06 - val_loss: 1.1043e-05
Epoch 168/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.3721e-05 - val_loss: 6.4711e-06
Epoch 169/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 8.0874e-06 - val_loss: 1.4516e-05
Epoch 170/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.1268e-05 - val_loss: 1.1083e-05
Epoch 171/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.8904e-05 - val_loss: 2.6908e-05
Epoch 172/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.4403e-05 - val_loss: 8.4103e-06
Epoch 173/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 6.6878e-06 - val_loss: 1.3387e-05
Epoch 174/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 6.4919e-05 - val_loss: 5.7586e-06
Epoch 175/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.7270e-06 - val_loss: 3.7061e-06
Epoch 176/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.5859e-06 - val_loss: 3.7121e-06
Epoch 177/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.4221e-05 - val_loss: 6.4429e-05
Epoch 178/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.6768e-05 - val_loss: 4.9492e-06
Epoch 179/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 8.4355e-06 - val_loss: 1.0123e-05
Epoch 180/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.8337e-05 - val_loss: 1.1122e-05
Epoch 181/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.1550e-05 - val_loss: 8.0207e-06
Epoch 182/1000
3888/3888 [==============================] - 25s 7ms/sample - loss: 4.9660e-06 - val_loss: 2.8889e-06
Epoch 183/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 6.0922e-06 - val_loss: 1.5808e-05
Epoch 184/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.4978e-05 - val_loss: 2.9725e-06
Epoch 185/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.8162e-05 - val_loss: 4.2424e-06
Epoch 186/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.0940e-06 - val_loss: 3.6263e-06
Epoch 187/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 7.7531e-06 - val_loss: 7.4437e-06
Epoch 188/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 9.4290e-06 - val_loss: 5.9724e-06
Epoch 189/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.5854e-05 - val_loss: 5.7907e-06
Epoch 190/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.3655e-05 - val_loss: 6.7966e-05
Epoch 191/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.4272e-05 - val_loss: 5.0592e-06
Epoch 192/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 9.8572e-06 - val_loss: 3.4861e-06
Epoch 193/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.5218e-05 - val_loss: 3.7197e-06
Epoch 194/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.2828e-06 - val_loss: 4.7497e-06
Epoch 195/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.0605e-05 - val_loss: 5.8219e-06
Epoch 196/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.9995e-06 - val_loss: 9.1424e-06
Epoch 197/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.6319e-05 - val_loss: 3.6229e-06
Epoch 198/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 6.2631e-06 - val_loss: 3.4180e-05
Epoch 199/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.6503e-05 - val_loss: 3.8556e-06
Epoch 200/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.5699e-06 - val_loss: 5.1344e-06
Epoch 201/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.0567e-05 - val_loss: 4.8763e-06
Epoch 202/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 9.7282e-06 - val_loss: 5.9419e-06
Epoch 203/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.2507e-05 - val_loss: 9.5628e-06
Epoch 204/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.4946e-06 - val_loss: 5.7195e-06
Epoch 205/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.4098e-05 - val_loss: 5.1858e-06
Epoch 206/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.8403e-05 - val_loss: 2.5995e-05
Epoch 207/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.3422e-05 - val_loss: 1.4062e-05
Epoch 208/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 9.5878e-06 - val_loss: 1.9267e-05
Epoch 209/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 8.4157e-06 - val_loss: 1.4055e-05
Epoch 210/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.3906e-05 - val_loss: 6.0141e-06
Epoch 211/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.0534e-05 - val_loss: 1.3446e-04
Epoch 212/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.5537e-05 - val_loss: 3.2733e-06
Epoch 213/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 7.7842e-06 - val_loss: 3.1914e-06
Epoch 214/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.6023e-05 - val_loss: 4.4483e-06
Epoch 215/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 8.0280e-06 - val_loss: 8.7618e-06
Epoch 216/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.8114e-05 - val_loss: 1.0879e-05
Epoch 217/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.0479e-05 - val_loss: 4.2874e-06
Epoch 218/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.5584e-06 - val_loss: 8.7401e-06
Epoch 219/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.8559e-04 - val_loss: 1.6975e-04
Epoch 220/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.7733e-05 - val_loss: 3.9709e-06
Epoch 221/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.2597e-06 - val_loss: 2.8213e-06
Epoch 222/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.5294e-06 - val_loss: 6.0359e-06
Epoch 223/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.1256e-06 - val_loss: 6.8854e-06
Epoch 224/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.0661e-06 - val_loss: 1.6016e-05
Epoch 225/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.2161e-05 - val_loss: 9.0677e-06
Epoch 226/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.1705e-06 - val_loss: 2.8227e-06
Epoch 227/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.2861e-06 - val_loss: 2.3906e-06
Epoch 228/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.0140e-06 - val_loss: 1.0936e-05
Epoch 229/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.1946e-05 - val_loss: 2.7610e-05
Epoch 230/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 8.9446e-06 - val_loss: 4.4194e-06
Epoch 231/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.0146e-05 - val_loss: 1.2693e-05
Epoch 232/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.0540e-05 - val_loss: 2.8234e-05
Epoch 233/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.5211e-05 - val_loss: 1.3661e-05
Epoch 234/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 7.6076e-06 - val_loss: 4.1647e-06
Epoch 235/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.3647e-05 - val_loss: 2.5881e-06
Epoch 236/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.7987e-05 - val_loss: 5.7978e-04
Epoch 237/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.8957e-05 - val_loss: 2.5036e-06
Epoch 238/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.6504e-06 - val_loss: 2.8019e-06
Epoch 239/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 7.5429e-06 - val_loss: 2.0502e-04
Epoch 240/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.0832e-05 - val_loss: 4.1547e-06
Epoch 241/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.1662e-06 - val_loss: 4.7692e-06
Epoch 242/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 8.1077e-06 - val_loss: 5.5972e-05
Epoch 243/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 6.2010e-06 - val_loss: 7.8137e-06
Epoch 244/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.1416e-05 - val_loss: 1.1026e-05
Epoch 245/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.8985e-05 - val_loss: 2.9961e-06
Epoch 246/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.7399e-06 - val_loss: 6.7300e-06
Epoch 247/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.6717e-06 - val_loss: 2.8875e-06
Epoch 248/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.5843e-05 - val_loss: 2.1691e-05
Epoch 249/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.1638e-06 - val_loss: 6.2934e-06
Epoch 250/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.2259e-05 - val_loss: 1.4772e-05
Epoch 251/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.6793e-05 - val_loss: 5.3935e-06
Epoch 252/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.0534e-06 - val_loss: 2.0750e-06
Epoch 253/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.2952e-06 - val_loss: 7.0304e-06
Epoch 254/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.0320e-05 - val_loss: 4.9352e-04
Epoch 255/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.6670e-05 - val_loss: 3.5650e-06
Epoch 256/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 6.9173e-06 - val_loss: 8.9296e-06
Epoch 257/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.4036e-05 - val_loss: 8.8567e-05
Epoch 258/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 8.2342e-06 - val_loss: 4.2321e-06
Epoch 259/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.8696e-06 - val_loss: 5.5012e-06
Epoch 260/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 6.6820e-06 - val_loss: 3.0284e-06
Epoch 261/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.7016e-05 - val_loss: 4.4189e-06
Epoch 262/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.8479e-06 - val_loss: 3.1388e-06
Epoch 263/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 7.4175e-06 - val_loss: 4.1705e-06
Epoch 264/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.6407e-05 - val_loss: 8.5656e-06
Epoch 265/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.5397e-06 - val_loss: 7.7751e-06
Epoch 266/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.6146e-05 - val_loss: 2.6952e-06
Epoch 267/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.8229e-06 - val_loss: 7.9935e-06
Epoch 268/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.9262e-05 - val_loss: 1.9356e-04
Epoch 269/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.1172e-05 - val_loss: 2.3722e-05
Epoch 270/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 9.7688e-06 - val_loss: 4.1298e-06
Epoch 271/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.4902e-05 - val_loss: 3.9850e-06
Epoch 272/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 8.8587e-06 - val_loss: 3.3881e-06
Epoch 273/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.0582e-05 - val_loss: 6.6808e-05
Epoch 274/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.8769e-05 - val_loss: 2.3674e-06
Epoch 275/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.1449e-05 - val_loss: 1.2749e-05
Epoch 276/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 7.0836e-06 - val_loss: 8.3561e-06
Epoch 277/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.5984e-06 - val_loss: 3.0729e-06
Epoch 278/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.7100e-05 - val_loss: 3.3114e-06
Epoch 279/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.0716e-06 - val_loss: 8.2138e-06
Epoch 280/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.0234e-05 - val_loss: 7.0866e-06
Epoch 281/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.7404e-05 - val_loss: 4.7518e-06
Epoch 282/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 7.7398e-06 - val_loss: 4.8252e-06
Epoch 283/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.5039e-05 - val_loss: 1.1410e-05
Epoch 284/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.9639e-06 - val_loss: 2.7529e-06
Epoch 285/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.9719e-06 - val_loss: 4.1002e-06
Epoch 286/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.4980e-05 - val_loss: 1.7641e-05
Epoch 287/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.5673e-05 - val_loss: 2.8693e-06
Epoch 288/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 6.8242e-06 - val_loss: 2.3793e-05
Epoch 289/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.6544e-05 - val_loss: 4.5206e-06
Epoch 290/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.6078e-06 - val_loss: 3.1448e-06
Epoch 291/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.9461e-06 - val_loss: 2.3433e-06
Epoch 292/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.9831e-06 - val_loss: 9.1226e-06
Epoch 293/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.4616e-05 - val_loss: 1.4062e-05
Epoch 294/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 7.9646e-06 - val_loss: 2.5544e-05
Epoch 295/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.9192e-05 - val_loss: 1.9452e-05
Epoch 296/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.2583e-05 - val_loss: 7.6355e-06
Epoch 297/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 7.3998e-06 - val_loss: 8.1842e-06
Epoch 298/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.6949e-05 - val_loss: 1.1132e-05
Epoch 299/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.6054e-05 - val_loss: 5.5370e-06
Epoch 300/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.9021e-06 - val_loss: 1.8910e-06
Epoch 301/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.0826e-05 - val_loss: 2.6743e-05
Epoch 302/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.9343e-05 - val_loss: 4.4653e-06
Epoch 303/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.6826e-06 - val_loss: 2.6318e-05
Epoch 304/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.2288e-05 - val_loss: 5.3681e-06
Epoch 305/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.3468e-06 - val_loss: 2.1665e-06
Epoch 306/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.3323e-06 - val_loss: 2.0804e-05
Epoch 307/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.0835e-05 - val_loss: 1.8382e-06
Epoch 308/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 7.2993e-06 - val_loss: 2.4535e-06
Epoch 309/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.9745e-05 - val_loss: 1.1990e-05
Epoch 310/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.5036e-06 - val_loss: 2.7715e-06
Epoch 311/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.4330e-05 - val_loss: 2.4529e-05
Epoch 312/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 9.7909e-06 - val_loss: 2.5502e-06
Epoch 313/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.8878e-05 - val_loss: 7.1583e-06
Epoch 314/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 6.2379e-06 - val_loss: 4.8045e-06
Epoch 315/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 7.2493e-06 - val_loss: 1.3291e-04
Epoch 316/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.5995e-05 - val_loss: 7.1144e-06
Epoch 317/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.9824e-06 - val_loss: 5.1017e-06
Epoch 318/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.1171e-05 - val_loss: 8.3160e-06
Epoch 319/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 8.6706e-06 - val_loss: 1.2412e-05
Epoch 320/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.0677e-05 - val_loss: 2.9676e-06
Epoch 321/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.2859e-06 - val_loss: 1.1344e-05
Epoch 322/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 8.0877e-06 - val_loss: 1.5857e-05
Epoch 323/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 9.8649e-06 - val_loss: 2.0032e-05
Epoch 324/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.5161e-05 - val_loss: 1.2625e-05
Epoch 325/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 7.1235e-06 - val_loss: 3.7669e-06
Epoch 326/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.7525e-05 - val_loss: 4.0101e-06
Epoch 327/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 9.0887e-06 - val_loss: 3.5193e-06
Epoch 328/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.6846e-05 - val_loss: 2.1912e-05
Epoch 329/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.8894e-06 - val_loss: 2.7197e-06
Epoch 330/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.7503e-05 - val_loss: 2.9765e-06
Epoch 331/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.7761e-06 - val_loss: 7.9608e-05
Epoch 332/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.5092e-05 - val_loss: 2.4658e-06
Epoch 333/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 7.2098e-06 - val_loss: 3.8985e-06
Epoch 334/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.4489e-05 - val_loss: 3.0874e-06
Epoch 335/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.7718e-06 - val_loss: 3.2098e-04
Epoch 336/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.7231e-05 - val_loss: 1.7458e-06
Epoch 337/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.1549e-06 - val_loss: 6.0096e-06
Epoch 338/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.5611e-06 - val_loss: 2.7532e-05
Epoch 339/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.3851e-06 - val_loss: 6.8447e-06
Epoch 340/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.3828e-05 - val_loss: 1.3944e-05
Epoch 341/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.7152e-05 - val_loss: 1.1251e-05
Epoch 342/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.8053e-06 - val_loss: 4.2677e-06
Epoch 343/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.4634e-06 - val_loss: 2.4049e-06
Epoch 344/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.7995e-05 - val_loss: 3.1839e-06
Epoch 345/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 8.5926e-06 - val_loss: 3.5819e-06
Epoch 346/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 6.2068e-06 - val_loss: 6.2199e-06
Epoch 347/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.3199e-05 - val_loss: 6.5630e-06
Epoch 348/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.9872e-06 - val_loss: 6.5838e-06
Epoch 349/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.4526e-06 - val_loss: 9.1056e-06
Epoch 350/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.0226e-05 - val_loss: 2.1736e-05
Epoch 351/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.6254e-05 - val_loss: 1.6571e-06
Epoch 352/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 9.8570e-06 - val_loss: 5.6372e-06
Epoch 353/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.9258e-05 - val_loss: 9.8708e-06
Epoch 354/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.4799e-06 - val_loss: 3.0529e-06
Epoch 355/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.8970e-05 - val_loss: 9.7103e-05
Epoch 356/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.0765e-05 - val_loss: 5.2303e-06
Epoch 357/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.3642e-06 - val_loss: 3.9316e-06
Epoch 358/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.7696e-05 - val_loss: 1.2809e-05
Epoch 359/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 6.5582e-06 - val_loss: 1.6188e-05
Epoch 360/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 6.6199e-06 - val_loss: 3.3366e-06
Epoch 361/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.5320e-05 - val_loss: 5.3130e-05
Epoch 362/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.3166e-05 - val_loss: 3.8429e-05
Epoch 363/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.1300e-05 - val_loss: 4.0415e-06
Epoch 364/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.8959e-06 - val_loss: 3.0321e-06
Epoch 365/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.9981e-06 - val_loss: 7.8480e-06
Epoch 366/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.2399e-05 - val_loss: 6.1186e-06
Epoch 367/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.8772e-05 - val_loss: 7.9795e-06
Epoch 368/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.8795e-06 - val_loss: 4.2210e-06
Epoch 369/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 6.7394e-06 - val_loss: 1.3257e-05
Epoch 370/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.1198e-05 - val_loss: 7.8698e-06
Epoch 371/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.4774e-06 - val_loss: 1.8981e-06
Epoch 372/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.4576e-06 - val_loss: 1.6571e-06
Epoch 373/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 9.3515e-06 - val_loss: 1.5787e-06
Epoch 374/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.3402e-06 - val_loss: 5.0556e-06
Epoch 375/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 8.4162e-06 - val_loss: 1.2430e-05
Epoch 376/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.9921e-06 - val_loss: 2.1811e-06
Epoch 377/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 9.7148e-06 - val_loss: 2.3975e-06
Epoch 378/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 8.8714e-06 - val_loss: 1.9462e-05
Epoch 379/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.0419e-05 - val_loss: 1.7294e-04
Epoch 380/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.6479e-05 - val_loss: 5.5537e-06
Epoch 381/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.7851e-06 - val_loss: 3.7647e-06
Epoch 382/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.0539e-05 - val_loss: 2.5294e-06
Epoch 383/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.2382e-05 - val_loss: 1.6727e-05
Epoch 384/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.5628e-06 - val_loss: 1.6156e-06
Epoch 385/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.4070e-06 - val_loss: 2.8558e-05
Epoch 386/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 8.0750e-06 - val_loss: 5.0933e-06
Epoch 387/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.7281e-05 - val_loss: 3.0829e-05
Epoch 388/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.1629e-05 - val_loss: 2.5182e-06
Epoch 389/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.3222e-06 - val_loss: 2.3697e-06
Epoch 390/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.2518e-06 - val_loss: 4.3680e-06
Epoch 391/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.7339e-05 - val_loss: 3.7992e-06
Epoch 392/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.1709e-05 - val_loss: 1.5287e-05
Epoch 393/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.7495e-05 - val_loss: 3.5470e-06
Epoch 394/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.3256e-06 - val_loss: 1.8248e-06
Epoch 395/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.2331e-05 - val_loss: 1.4636e-05
Epoch 396/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 6.2546e-05 - val_loss: 1.0844e-04
Epoch 397/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 8.1907e-06 - val_loss: 2.0723e-06
Epoch 398/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.5610e-06 - val_loss: 1.4328e-06
Epoch 399/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.5477e-06 - val_loss: 2.1586e-06
Epoch 400/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.8732e-06 - val_loss: 2.8425e-06
Epoch 401/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.3002e-06 - val_loss: 4.4867e-06
Epoch 402/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.0430e-05 - val_loss: 7.6091e-06
Epoch 403/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 6.9386e-06 - val_loss: 1.6681e-05
Epoch 404/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.7116e-05 - val_loss: 3.2354e-06
Epoch 405/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.1029e-05 - val_loss: 2.9025e-06
Epoch 406/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.8388e-05 - val_loss: 7.9700e-06
Epoch 407/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.7480e-06 - val_loss: 3.6721e-06
Epoch 408/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.2930e-06 - val_loss: 1.2327e-06
Epoch 409/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.5372e-06 - val_loss: 9.5345e-06
Epoch 410/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.2082e-05 - val_loss: 1.7983e-06
Epoch 411/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.9837e-06 - val_loss: 2.2540e-06
Epoch 412/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.9781e-06 - val_loss: 8.1043e-06
Epoch 413/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.6810e-05 - val_loss: 1.7045e-06
Epoch 414/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.1446e-05 - val_loss: 2.9517e-06
Epoch 415/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.9160e-06 - val_loss: 1.1618e-05
Epoch 416/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.0410e-06 - val_loss: 2.7848e-06
Epoch 417/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.0668e-05 - val_loss: 1.6369e-05
Epoch 418/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 8.2180e-06 - val_loss: 4.3633e-06
Epoch 419/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.1093e-06 - val_loss: 4.9985e-06
Epoch 420/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.4571e-05 - val_loss: 2.1615e-06
Epoch 421/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.5644e-06 - val_loss: 3.4162e-06
Epoch 422/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.1330e-05 - val_loss: 1.8454e-06
Epoch 423/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.9444e-06 - val_loss: 9.8210e-06
Epoch 424/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 9.6361e-06 - val_loss: 3.3164e-06
Epoch 425/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 9.5957e-06 - val_loss: 2.8945e-05
Epoch 426/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.0282e-05 - val_loss: 1.7842e-05
Epoch 427/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.6159e-06 - val_loss: 2.3305e-05
Epoch 428/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.8766e-05 - val_loss: 1.2350e-04
Epoch 429/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 7.0834e-06 - val_loss: 5.1694e-05
Epoch 430/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.0295e-05 - val_loss: 1.3140e-06
Epoch 431/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.9117e-06 - val_loss: 3.7196e-05
Epoch 432/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.2866e-05 - val_loss: 2.3805e-06
Epoch 433/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.1379e-06 - val_loss: 2.6302e-06
Epoch 434/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.5981e-06 - val_loss: 1.4342e-05
Epoch 435/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.6523e-05 - val_loss: 2.7720e-06
Epoch 436/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.3803e-06 - val_loss: 2.5773e-06
Epoch 437/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.1968e-06 - val_loss: 1.0434e-05
Epoch 438/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.3427e-05 - val_loss: 1.0898e-05
Epoch 439/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 8.7490e-06 - val_loss: 2.0043e-05
Epoch 440/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.1962e-05 - val_loss: 6.5810e-06
Epoch 441/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.7905e-06 - val_loss: 1.7469e-06
Epoch 442/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.5582e-06 - val_loss: 2.1498e-05
Epoch 443/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.8829e-05 - val_loss: 2.5506e-06
Epoch 444/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.4901e-06 - val_loss: 1.5371e-06
Epoch 445/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.6763e-05 - val_loss: 4.0585e-06
Epoch 446/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.9339e-06 - val_loss: 2.9940e-06
Epoch 447/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.9427e-06 - val_loss: 9.7076e-06
Epoch 448/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 8.6872e-06 - val_loss: 4.2538e-06
Epoch 449/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.4074e-06 - val_loss: 1.6469e-06
Epoch 450/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.3592e-05 - val_loss: 5.6535e-06
Epoch 451/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.5212e-06 - val_loss: 1.8185e-05
Epoch 452/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.8466e-05 - val_loss: 5.6691e-05
Epoch 453/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 9.7948e-06 - val_loss: 2.2572e-06
Epoch 454/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.5620e-06 - val_loss: 5.9396e-05
Epoch 455/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.8740e-05 - val_loss: 3.2102e-06
Epoch 456/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 8.8863e-06 - val_loss: 3.0595e-05
Epoch 457/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.3089e-05 - val_loss: 4.4024e-06
Epoch 458/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.6096e-06 - val_loss: 1.3994e-06
Epoch 459/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.2700e-06 - val_loss: 1.3073e-06
Epoch 460/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.8707e-06 - val_loss: 1.9146e-06
Epoch 461/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.7898e-06 - val_loss: 4.9201e-06
Epoch 462/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.9175e-06 - val_loss: 1.1871e-06
Epoch 463/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.7909e-05 - val_loss: 3.0527e-06
Epoch 464/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.1629e-06 - val_loss: 2.2708e-06
Epoch 465/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.5498e-06 - val_loss: 3.2730e-06
Epoch 466/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.3103e-05 - val_loss: 1.8674e-05
Epoch 467/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.6882e-06 - val_loss: 1.5706e-06
Epoch 468/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.2193e-05 - val_loss: 2.8550e-06
Epoch 469/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.9752e-06 - val_loss: 2.3210e-06
Epoch 470/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 8.9457e-06 - val_loss: 4.0313e-05
Epoch 471/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.0220e-05 - val_loss: 2.6629e-06
Epoch 472/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.1160e-06 - val_loss: 2.7336e-06
Epoch 473/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.2549e-05 - val_loss: 6.9065e-06
Epoch 474/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.7380e-05 - val_loss: 2.7181e-05
Epoch 475/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.1516e-06 - val_loss: 1.9019e-06
Epoch 476/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.2201e-06 - val_loss: 8.4208e-06
Epoch 477/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.6630e-05 - val_loss: 3.5877e-05
Epoch 478/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 9.3373e-06 - val_loss: 6.2671e-06
Epoch 479/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.2239e-06 - val_loss: 1.1050e-06
Epoch 480/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.2317e-06 - val_loss: 3.7757e-06
Epoch 481/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.1544e-05 - val_loss: 9.3099e-05
Epoch 482/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 8.2510e-06 - val_loss: 2.6037e-06
Epoch 483/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.9229e-06 - val_loss: 2.4616e-06
Epoch 484/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 8.7306e-06 - val_loss: 2.3002e-06
Epoch 485/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.2941e-05 - val_loss: 1.6621e-06
Epoch 486/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.5915e-06 - val_loss: 1.8730e-06
Epoch 487/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.3155e-06 - val_loss: 2.6492e-06
Epoch 488/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 7.4967e-06 - val_loss: 1.4532e-05
Epoch 489/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.0199e-05 - val_loss: 6.8134e-06
Epoch 490/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 8.5593e-06 - val_loss: 2.7726e-06
Epoch 491/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.3992e-05 - val_loss: 4.2188e-06
Epoch 492/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.8455e-06 - val_loss: 2.5779e-06
Epoch 493/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.7420e-06 - val_loss: 7.9425e-06
Epoch 494/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.5136e-05 - val_loss: 3.6791e-06
Epoch 495/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.9405e-06 - val_loss: 3.0632e-06
Epoch 496/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.2946e-06 - val_loss: 1.3207e-06
Epoch 497/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.7566e-06 - val_loss: 2.6618e-06
Epoch 498/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.1508e-06 - val_loss: 1.0827e-05
Epoch 499/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.8777e-05 - val_loss: 2.9026e-06
Epoch 500/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.6672e-06 - val_loss: 1.7816e-06
Epoch 501/1000
3888/3888 [==============================] - 25s 7ms/sample - loss: 1.0302e-05 - val_loss: 4.9677e-06
Epoch 502/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.0963e-05 - val_loss: 4.2461e-06
Epoch 503/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 7.1117e-06 - val_loss: 8.4581e-06
Epoch 504/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.7325e-06 - val_loss: 8.4431e-06
Epoch 505/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.0685e-05 - val_loss: 3.4090e-06
Epoch 506/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.6380e-06 - val_loss: 4.6476e-06
Epoch 507/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.0606e-06 - val_loss: 1.4625e-06
Epoch 508/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.0591e-06 - val_loss: 6.3639e-06
Epoch 509/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.1879e-05 - val_loss: 3.5000e-04
Epoch 510/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.1970e-05 - val_loss: 2.4689e-06
Epoch 511/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.7259e-06 - val_loss: 1.4516e-06
Epoch 512/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.9112e-06 - val_loss: 2.4964e-05
Epoch 513/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.3963e-05 - val_loss: 1.5810e-06
Epoch 514/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.7692e-05 - val_loss: 3.6818e-05
Epoch 515/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.1967e-06 - val_loss: 1.5123e-06
Epoch 516/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.1382e-06 - val_loss: 4.2376e-06
Epoch 517/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 9.3197e-06 - val_loss: 2.6531e-06
Epoch 518/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.5247e-06 - val_loss: 4.4808e-06
Epoch 519/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 6.4625e-06 - val_loss: 8.5993e-06
Epoch 520/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 6.0658e-06 - val_loss: 5.5232e-06
Epoch 521/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.0766e-05 - val_loss: 3.2520e-06
Epoch 522/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.9603e-06 - val_loss: 2.1044e-06
Epoch 523/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.2425e-06 - val_loss: 5.5724e-06
Epoch 524/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.1037e-05 - val_loss: 1.6748e-06
Epoch 525/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.1982e-06 - val_loss: 2.3041e-05
Epoch 526/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.1935e-05 - val_loss: 2.1598e-06
Epoch 527/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.7640e-06 - val_loss: 6.9504e-06
Epoch 528/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.5524e-05 - val_loss: 1.6832e-05
Epoch 529/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.8794e-06 - val_loss: 2.7627e-06
Epoch 530/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 7.1826e-06 - val_loss: 5.9030e-05
Epoch 531/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.5602e-05 - val_loss: 0.0013
Epoch 532/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.7204e-05 - val_loss: 1.9543e-06
Epoch 533/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.9748e-06 - val_loss: 2.5636e-06
Epoch 534/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 7.4485e-06 - val_loss: 8.0238e-06
Epoch 535/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.0148e-06 - val_loss: 1.0977e-06
Epoch 536/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.8297e-06 - val_loss: 3.1114e-06
Epoch 537/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.7279e-05 - val_loss: 3.4108e-06
Epoch 538/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.3361e-06 - val_loss: 3.7248e-06
Epoch 539/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 6.8194e-06 - val_loss: 4.5008e-06
Epoch 540/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 9.4833e-06 - val_loss: 1.6702e-06
Epoch 541/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.9758e-06 - val_loss: 1.7727e-04
Epoch 542/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.6670e-05 - val_loss: 3.5155e-06
Epoch 543/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.1810e-06 - val_loss: 2.6525e-06
Epoch 544/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.6608e-06 - val_loss: 1.9859e-06
Epoch 545/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.5788e-06 - val_loss: 1.3038e-04
Epoch 546/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.1779e-05 - val_loss: 1.6578e-05
Epoch 547/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 7.9256e-06 - val_loss: 2.2667e-05
Epoch 548/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 7.6737e-06 - val_loss: 3.6928e-06
Epoch 549/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 7.2907e-06 - val_loss: 4.1552e-05
Epoch 550/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 8.8646e-06 - val_loss: 7.4832e-06
Epoch 551/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.9641e-06 - val_loss: 5.9681e-05
Epoch 552/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.2696e-05 - val_loss: 1.5658e-06
Epoch 553/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.6605e-05 - val_loss: 2.3973e-05
Epoch 554/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 6.5548e-06 - val_loss: 1.6522e-05
Epoch 555/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.6798e-06 - val_loss: 3.7012e-06
Epoch 556/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 6.6500e-06 - val_loss: 1.1675e-06
Epoch 557/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.6407e-05 - val_loss: 1.2810e-05
Epoch 558/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.5565e-06 - val_loss: 2.2760e-06
Epoch 559/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.1756e-05 - val_loss: 1.2330e-05
Epoch 560/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 7.3257e-06 - val_loss: 2.0348e-06
Epoch 561/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.8081e-06 - val_loss: 1.9160e-06
Epoch 562/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.1537e-05 - val_loss: 4.3128e-06
Epoch 563/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.6935e-06 - val_loss: 2.2248e-06
Epoch 564/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.5494e-05 - val_loss: 1.4273e-06
Epoch 565/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.9940e-05 - val_loss: 4.6616e-06
Epoch 566/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.4349e-06 - val_loss: 2.0059e-06
Epoch 567/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.0320e-06 - val_loss: 2.2421e-06
Epoch 568/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.5344e-05 - val_loss: 5.3847e-06
Epoch 569/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.2267e-06 - val_loss: 1.6299e-06
Epoch 570/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.4201e-05 - val_loss: 2.1310e-06
Epoch 571/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.6596e-06 - val_loss: 3.2991e-06
Epoch 572/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.6744e-05 - val_loss: 1.8938e-06
Epoch 573/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.5270e-06 - val_loss: 1.1681e-06
Epoch 574/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.1897e-05 - val_loss: 2.5746e-05
Epoch 575/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.3719e-06 - val_loss: 2.2581e-06
Epoch 576/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.0957e-06 - val_loss: 2.2371e-06
Epoch 577/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.9411e-06 - val_loss: 1.8941e-06
Epoch 578/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.7142e-05 - val_loss: 1.8515e-06
Epoch 579/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 6.8793e-06 - val_loss: 6.5662e-06
Epoch 580/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.8135e-06 - val_loss: 2.0197e-06
Epoch 581/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.6701e-06 - val_loss: 2.8080e-06
Epoch 582/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 9.5456e-06 - val_loss: 3.4867e-06
Epoch 583/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.6729e-05 - val_loss: 2.6111e-05
Epoch 584/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.0804e-05 - val_loss: 2.4409e-06
Epoch 585/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.7569e-06 - val_loss: 2.7870e-06
Epoch 586/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.3594e-06 - val_loss: 8.9507e-06
Epoch 587/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 6.7586e-06 - val_loss: 1.9234e-05
Epoch 588/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.9098e-05 - val_loss: 4.0467e-06
Epoch 589/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.1890e-06 - val_loss: 1.8381e-06
Epoch 590/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 9.4156e-06 - val_loss: 2.6882e-06
Epoch 591/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 7.2541e-06 - val_loss: 1.3074e-05
Epoch 592/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 9.6957e-06 - val_loss: 3.3823e-06
Epoch 593/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.2607e-05 - val_loss: 4.8906e-06
Epoch 594/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.0421e-05 - val_loss: 1.6661e-05
Epoch 595/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.1893e-06 - val_loss: 2.2540e-06
Epoch 596/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.6894e-06 - val_loss: 5.1744e-05
Epoch 597/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.9054e-05 - val_loss: 5.1232e-06
Epoch 598/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.6281e-06 - val_loss: 3.9606e-06
Epoch 599/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.3483e-06 - val_loss: 1.3690e-05
Epoch 600/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.4790e-05 - val_loss: 2.4143e-06
Epoch 601/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.3944e-06 - val_loss: 3.2145e-06
Epoch 602/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.6352e-05 - val_loss: 3.4554e-05
Epoch 603/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.1401e-05 - val_loss: 1.5602e-06
Epoch 604/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.8149e-06 - val_loss: 6.9155e-06
Epoch 605/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.3295e-06 - val_loss: 8.0885e-06
Epoch 606/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.9363e-05 - val_loss: 2.0667e-05
Epoch 607/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 6.8090e-06 - val_loss: 3.8826e-06
Epoch 608/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.9203e-06 - val_loss: 2.7585e-06
Epoch 609/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.4750e-06 - val_loss: 2.9368e-06
Epoch 610/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.8326e-05 - val_loss: 3.8102e-06
Epoch 611/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.4699e-06 - val_loss: 3.3834e-06
Epoch 612/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.3672e-06 - val_loss: 2.0387e-06
Epoch 613/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.7659e-05 - val_loss: 9.2670e-05
Epoch 614/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.9818e-06 - val_loss: 3.6425e-06
Epoch 615/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.5519e-06 - val_loss: 2.3307e-06
Epoch 616/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.3608e-06 - val_loss: 5.8237e-06
Epoch 617/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.9086e-05 - val_loss: 1.2601e-05
Epoch 618/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.3313e-06 - val_loss: 2.3233e-06
Epoch 619/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 6.4432e-06 - val_loss: 3.7451e-06
Epoch 620/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.2723e-06 - val_loss: 1.7335e-06
Epoch 621/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 8.9868e-06 - val_loss: 6.9475e-06
Epoch 622/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.0114e-05 - val_loss: 9.3069e-06
Epoch 623/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.8124e-05 - val_loss: 1.7672e-06
Epoch 624/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.3989e-06 - val_loss: 1.1625e-06
Epoch 625/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.6438e-06 - val_loss: 1.7478e-06
Epoch 626/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.7283e-05 - val_loss: 1.4054e-05
Epoch 627/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.7362e-06 - val_loss: 4.6295e-06
Epoch 628/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.7601e-05 - val_loss: 3.3281e-06
Epoch 629/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.5111e-06 - val_loss: 1.5195e-06
Epoch 630/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.0133e-06 - val_loss: 3.9153e-06
Epoch 631/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.5357e-06 - val_loss: 2.0025e-06
Epoch 632/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.1802e-05 - val_loss: 6.7810e-06
Epoch 633/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.5305e-06 - val_loss: 1.2397e-06
Epoch 634/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.7975e-06 - val_loss: 1.0428e-06
Epoch 635/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.3612e-06 - val_loss: 2.1498e-06
Epoch 636/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.4041e-06 - val_loss: 1.5012e-06
Epoch 637/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.6371e-05 - val_loss: 2.4546e-06
Epoch 638/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 8.7082e-06 - val_loss: 2.3361e-06
Epoch 639/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.3605e-05 - val_loss: 1.2134e-04
Epoch 640/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 7.8022e-06 - val_loss: 2.6426e-06
Epoch 641/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.3676e-06 - val_loss: 1.5212e-06
Epoch 642/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.9690e-06 - val_loss: 6.8162e-06
Epoch 643/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.5145e-05 - val_loss: 1.6881e-06
Epoch 644/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 7.0873e-06 - val_loss: 2.1053e-06
Epoch 645/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.9724e-06 - val_loss: 2.2792e-06
Epoch 646/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 6.1464e-06 - val_loss: 5.7513e-06
Epoch 647/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.5772e-05 - val_loss: 2.4785e-06
Epoch 648/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.4268e-06 - val_loss: 2.7976e-06
Epoch 649/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.6196e-06 - val_loss: 2.4775e-06
Epoch 650/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 8.7566e-06 - val_loss: 1.3547e-06
Epoch 651/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.0992e-06 - val_loss: 6.2714e-05
Epoch 652/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 8.1923e-06 - val_loss: 7.2175e-06
Epoch 653/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.9561e-06 - val_loss: 1.9828e-06
Epoch 654/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.5787e-05 - val_loss: 2.0099e-06
Epoch 655/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.0915e-06 - val_loss: 2.1493e-06
Epoch 656/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.9732e-06 - val_loss: 9.0273e-07
Epoch 657/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.7884e-06 - val_loss: 3.6888e-06
Epoch 658/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 8.9629e-06 - val_loss: 1.7047e-06
Epoch 659/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.2508e-06 - val_loss: 6.4187e-06
Epoch 660/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.4510e-05 - val_loss: 9.3093e-05
Epoch 661/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 7.0446e-06 - val_loss: 2.6692e-06
Epoch 662/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.5187e-06 - val_loss: 2.9071e-06
Epoch 663/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.7358e-06 - val_loss: 1.4749e-06
Epoch 664/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.2927e-06 - val_loss: 2.4128e-06
Epoch 665/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.3654e-06 - val_loss: 3.9152e-06
Epoch 666/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.2475e-05 - val_loss: 6.8375e-06
Epoch 667/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.8612e-06 - val_loss: 2.2017e-06
Epoch 668/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 9.6217e-06 - val_loss: 5.8312e-05
Epoch 669/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 9.6991e-06 - val_loss: 2.1325e-06
Epoch 670/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.6007e-06 - val_loss: 1.4804e-05
Epoch 671/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 7.1047e-06 - val_loss: 1.4811e-06
Epoch 672/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 7.0535e-06 - val_loss: 3.1895e-06
Epoch 673/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.0231e-05 - val_loss: 2.0922e-06
Epoch 674/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 7.7551e-06 - val_loss: 4.2293e-05
Epoch 675/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 9.0426e-06 - val_loss: 6.0160e-06
Epoch 676/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.3599e-06 - val_loss: 1.2902e-06
Epoch 677/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.8338e-06 - val_loss: 1.0942e-06
Epoch 678/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.4045e-05 - val_loss: 3.4290e-06
Epoch 679/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 8.4639e-06 - val_loss: 3.8250e-06
Epoch 680/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 8.9980e-06 - val_loss: 4.8498e-06
Epoch 681/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.9393e-06 - val_loss: 4.3163e-06
Epoch 682/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 8.3280e-06 - val_loss: 1.7860e-06
Epoch 683/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.6617e-05 - val_loss: 3.0099e-05
Epoch 684/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.6069e-06 - val_loss: 3.3856e-06
Epoch 685/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.0469e-05 - val_loss: 2.4201e-06
Epoch 686/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.5942e-06 - val_loss: 1.5469e-06
Epoch 687/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.4221e-06 - val_loss: 1.6682e-06
Epoch 688/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 7.9975e-06 - val_loss: 1.2484e-05
Epoch 689/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.3706e-05 - val_loss: 5.4635e-06
Epoch 690/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.1193e-06 - val_loss: 3.9881e-06
Epoch 691/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.9073e-06 - val_loss: 2.4884e-06
Epoch 692/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.0081e-05 - val_loss: 6.6150e-06
Epoch 693/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.9668e-05 - val_loss: 2.3831e-06
Epoch 694/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.6483e-06 - val_loss: 1.9681e-06
Epoch 695/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.5607e-06 - val_loss: 2.0258e-05
Epoch 696/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.2006e-05 - val_loss: 2.4010e-05
Epoch 697/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 7.0877e-06 - val_loss: 1.1488e-06
Epoch 698/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.7557e-06 - val_loss: 1.8847e-06
Epoch 699/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.5033e-06 - val_loss: 5.9241e-06
Epoch 700/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 6.3913e-06 - val_loss: 5.7067e-05
Epoch 701/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.0496e-05 - val_loss: 2.0991e-06
Epoch 702/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.8951e-06 - val_loss: 1.1018e-05
Epoch 703/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.8410e-06 - val_loss: 1.1916e-05
Epoch 704/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 7.3333e-06 - val_loss: 1.2308e-05
Epoch 705/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 8.0848e-06 - val_loss: 4.4458e-06
Epoch 706/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 7.5756e-06 - val_loss: 1.2734e-06
Epoch 707/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.6885e-06 - val_loss: 2.1342e-05
Epoch 708/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.8995e-05 - val_loss: 1.7240e-06
Epoch 709/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.1983e-06 - val_loss: 5.0669e-06
Epoch 710/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.9963e-06 - val_loss: 1.1463e-06
Epoch 711/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.4037e-05 - val_loss: 2.8233e-06
Epoch 712/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.9458e-06 - val_loss: 1.7376e-06
Epoch 713/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 7.2944e-06 - val_loss: 2.6379e-06
Epoch 714/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.2434e-06 - val_loss: 1.2183e-06
Epoch 715/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.2757e-06 - val_loss: 1.3196e-05
Epoch 716/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.2646e-05 - val_loss: 1.0804e-05
Epoch 717/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.5773e-06 - val_loss: 2.1277e-06
Epoch 718/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.1450e-05 - val_loss: 1.5117e-06
Epoch 719/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.8286e-06 - val_loss: 2.1248e-06
Epoch 720/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 9.9266e-06 - val_loss: 1.5344e-05
Epoch 721/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.4837e-06 - val_loss: 1.3613e-06
Epoch 722/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 7.3368e-06 - val_loss: 1.0740e-05
Epoch 723/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.7202e-06 - val_loss: 4.9885e-06
Epoch 724/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 9.8470e-06 - val_loss: 1.7056e-05
Epoch 725/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.2041e-05 - val_loss: 5.5972e-06
Epoch 726/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.9560e-06 - val_loss: 1.6328e-06
Epoch 727/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.8258e-06 - val_loss: 3.8789e-06
Epoch 728/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.1156e-05 - val_loss: 2.1728e-06
Epoch 729/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.2720e-05 - val_loss: 4.2272e-06
Epoch 730/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.8056e-06 - val_loss: 8.4858e-06
Epoch 731/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.1093e-06 - val_loss: 1.9013e-06
Epoch 732/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 7.7393e-06 - val_loss: 8.4994e-06
Epoch 733/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 6.7974e-06 - val_loss: 3.6759e-06
Epoch 734/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.1015e-06 - val_loss: 2.7462e-05
Epoch 735/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.0916e-05 - val_loss: 1.4867e-06
Epoch 736/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.0871e-06 - val_loss: 2.1985e-06
Epoch 737/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.0223e-05 - val_loss: 5.1013e-06
Epoch 738/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.0697e-06 - val_loss: 1.2254e-06
Epoch 739/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.5044e-06 - val_loss: 5.0729e-06
Epoch 740/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.2617e-05 - val_loss: 6.2119e-06
Epoch 741/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.2197e-06 - val_loss: 1.0290e-05
Epoch 742/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.5227e-05 - val_loss: 4.5622e-06
Epoch 743/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.0746e-06 - val_loss: 2.5829e-05
Epoch 744/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 6.6144e-06 - val_loss: 2.1563e-06
Epoch 745/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.6209e-06 - val_loss: 8.2831e-06
Epoch 746/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.4346e-05 - val_loss: 2.6101e-06
Epoch 747/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.5366e-06 - val_loss: 1.5143e-06
Epoch 748/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 3.2409e-06 - val_loss: 5.0632e-06
Epoch 749/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 8.2085e-06 - val_loss: 1.0033e-04
Epoch 750/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 5.4396e-06 - val_loss: 3.2083e-06
Epoch 751/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.4178e-06 - val_loss: 1.3873e-05
Epoch 752/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 1.5007e-05 - val_loss: 1.1392e-06
Epoch 753/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.1566e-06 - val_loss: 8.0894e-06
Epoch 754/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 6.3194e-06 - val_loss: 2.9919e-05
Epoch 755/1000
3888/3888 [==============================] - 25s 6ms/sample - loss: 4.2546e-05 - val_loss: 5.6485e-06
Epoch 756/1000
3872/3888 [============================>.] - ETA: 0s - loss: 2.1037e-06Restoring model weights from the end of the best epoch.
3888/3888 [==============================] - 25s 6ms/sample - loss: 2.0990e-06 - val_loss: 1.0192e-06
Epoch 00756: early stopping
In [69]:
print(history.history.keys())
print('best value: ', conv_ae.evaluate(X_train, X_train, verbose=0))


pd.DataFrame(history.history).plot(figsize=(8, 5), logy=True)
plt.grid()
dict_keys(['loss', 'val_loss'])
best value:  9.027328154536509e-07
In [70]:
X_reconstructions = conv_ae.predict(X_train)
X_reconstructions = stdscaler.inverse_transform(np.moveaxis(X_reconstructions,3,1).reshape(len(times),len(group)*nl*nc))
calculateerror(X_train_1D.reshape(len(times),len(groups),nl,nc), 
               X_reconstructions.reshape(len(times),len(groups),nl,nc), 
               groups,
               print_step=0)
max_abs_error:  6.7890625
mean_abs_error:  0.014291764742296214
/home/viluiz/anaconda3/envs/py3ml/lib/python3.7/site-packages/ipykernel_launcher.py:3: RuntimeWarning: divide by zero encountered in true_divide
  This is separate from the ipykernel package so we can avoid doing imports until
/home/viluiz/anaconda3/envs/py3ml/lib/python3.7/site-packages/ipykernel_launcher.py:3: RuntimeWarning: invalid value encountered in true_divide
  This is separate from the ipykernel package so we can avoid doing imports until
In [110]:
X_train_encoded = conv_ae.layers[0].predict(X_train)

fig, ax = plt.subplots(1,1, figsize=[20,10])
ax.plot(times, X_train_encoded);
ax.grid()
ax.legend(range(15))
Out[110]:
<matplotlib.legend.Legend at 0x7ff80dff4650>
In [113]:
from tensorflow.keras.models import load_model
conv_ae.save("conv_ae.h5")
import joblib
joblib.dump(stdscaler, "stdscaler.pkl")
np.savetxt('X_train_encoded.csv', X_train_encoded, delimiter=',') 
np.save('X_train.npy', X_train) 

#...
# conv_ae = load_model("conv_ae.h5") 
# stdscaler = joblib.load("stdscaler.pkl") 
# X_train_compressed = np.loadtxt('X_train_encoded.csv', delimiter=',') 

# X_train_1D = np.loadtxt('X_train_1D.csv', delimiter=',') 
# times  = np.loadtxt('times.csv', delimiter=',') 
# with open('groups.txt') as f:
#     groups = [g.strip() for g in f.readlines()]
    
# X_recovered = conv_ae.layers[1].predict(X_train_compressed)
# X_recovered = stdscaler.inverse_transform(np.moveaxis(X_recovered,3,1).reshape(len(times),len(group)*nl*nc))   
In [71]:
fig, ax = plt.subplots(2,4, figsize=[20,10])
for i, group in enumerate(groups):
    im = ax.flatten()[i].imshow(X_reconstructions.reshape(len(times),len(groups),nl,nc)[100,i,:,:])
    fig.colorbar(im, ax=ax.flatten()[i])
    ax.flatten()[i].set_title(group)
In [72]:
fig, ax = plt.subplots(2,4, figsize=[20,10])
for i, group in enumerate(groups):
    ax.flatten()[i].plot(times, X_reconstructions[:,i*nl*nc+4])
    ax.flatten()[i].set_title(group)
In [93]:
fig, ax = plt.subplots(4,2, figsize=[20,40])
for i, group in enumerate(groups):
    ax.flatten()[i].plot(times, X_train_1D[:,i*nl*nc+4])
    ax.flatten()[i].plot(times, X_reconstructions[:,i*nl*nc+4],'--')
    ax.flatten()[i].set_title(group)
In [ ]: